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Wyświetlanie 21-40 z 311284
Tytuł:
Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep Learning.
Autorzy:
Lee DS; AI Grand ICT Center, Dong-Eui University, Busan 47340, Republic of Korea.
Kwon SK; Department of Computer Software Engineering, Dong-Eui University, Busan 47340, Republic of Korea.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 22; Vol. 24 (7). Date of Electronic Publication: 2024 Mar 22.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Computer Simulation ; Food ; Postprandial Period ; Eating
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.
Autorzy:
Yuan S; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Fan S; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Deng Z; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Pan P; School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 22; Vol. 24 (7). Date of Electronic Publication: 2024 Mar 22.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Heart Rate ; Radar ; Heart Rate Determination ; Algorithms
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Multimodal deep learning-based diagnostic model for BPPV.
Autorzy:
Lu H; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China.
Mao Y; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China. .
Li J; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China.
Zhu L; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China.
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Źródło:
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Mar 21; Vol. 24 (1), pp. 82. Date of Electronic Publication: 2024 Mar 21.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Nystagmus, Pathologic*/diagnosis
Humans ; Benign Paroxysmal Positional Vertigo/diagnosis ; Artificial Intelligence ; Hospitals
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.
Autorzy:
Arvidsson I; Centre for Mathematical Sciences, Lund University, Lund, Sweden. .
Strandberg O; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.
Palmqvist S; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Stomrud E; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Cullen N; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.
Janelidze S; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.
Tideman P; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Heyden A; Centre for Mathematical Sciences, Lund University, Lund, Sweden.
Åström K; Centre for Mathematical Sciences, Lund University, Lund, Sweden.
Hansson O; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
Mattsson-Carlgren N; Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden. .; Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden. .; Department of Neurology, Skåne University Hospital, Lund, Sweden. .
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Źródło:
Alzheimer's research & therapy [Alzheimers Res Ther] 2024 Mar 19; Vol. 16 (1), pp. 61. Date of Electronic Publication: 2024 Mar 19.
Typ publikacji:
Journal Article
MeSH Terms:
Alzheimer Disease*/complications
Alzheimer Disease*/diagnostic imaging
Deep Learning*
Cognitive Dysfunction*/diagnosis
Humans ; Biomarkers ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; Brain/pathology ; Cognition ; Atrophy/pathology ; Disease Progression
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.
Autorzy:
Zhang XF; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Wu HY; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Liang XW; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Chen JL; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
Li J; Radiology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
Zhang S; Pathology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
Liu Z; Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China. .; Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China. .
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Źródło:
BMC women's health [BMC Womens Health] 2024 Mar 19; Vol. 24 (1), pp. 182. Date of Electronic Publication: 2024 Mar 19.
Typ publikacji:
Journal Article
MeSH Terms:
Carcinoma, Squamous Cell*/diagnostic imaging
Carcinoma, Squamous Cell*/radiotherapy
Deep Learning*
Uterine Cervical Neoplasms*/diagnostic imaging
Uterine Cervical Neoplasms*/radiotherapy
Female ; Humans ; Radiotherapy, Adjuvant ; Radiomics ; Magnetic Resonance Imaging ; Retrospective Studies
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study.
Autorzy:
Zeng J; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Li K; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Cao F; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Zheng Y; Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 19; Vol. 14 (1), pp. 6609. Date of Electronic Publication: 2024 Mar 19.
Typ publikacji:
Journal Article
MeSH Terms:
Gastrointestinal Stromal Tumors*
Deep Learning*
Humans ; Prognosis ; Area Under Curve ; Calibration ; Nomograms ; SEER Program
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Predictive deep learning models for cognitive risk using accessible data.
Autorzy:
Karako K; Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
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Źródło:
Bioscience trends [Biosci Trends] 2024 Mar 19; Vol. 18 (1), pp. 66-72. Date of Electronic Publication: 2024 Feb 20.
Typ publikacji:
Journal Article
MeSH Terms:
Dementia*/diagnosis
Deep Learning*
Cognitive Dysfunction*/diagnosis
Cognitive Dysfunction*/psychology
Alzheimer Disease*/diagnosis
Humans ; Cognition ; Neuropsychological Tests ; Disease Progression
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Deep Learning Powered Identification of Differentiated Early Mesoderm Cells from Pluripotent Stem Cells.
Autorzy:
Mohammad S; School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.
Roy A; School of Mechanical, Aerospace, and Materials Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.
Karatzas A; School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.
Sarver SL; School of Mechanical, Aerospace, and Materials Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.
Anagnostopoulos I; School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.
Chowdhury F; School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.; School of Mechanical, Aerospace, and Materials Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.
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Źródło:
Cells [Cells] 2024 Mar 18; Vol. 13 (6). Date of Electronic Publication: 2024 Mar 18.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Pluripotent Stem Cells*
Animals ; Mice ; Cell Differentiation/physiology ; Germ Layers/metabolism ; Mesoderm/metabolism
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
An automated ICU agitation monitoring system for video streaming using deep learning classification.
Autorzy:
Dai PY; Department of Computer Science, Tunghai University, Taichung, Taiwan.
Wu YC; Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
Sheu RK; Department of Computer Science, Tunghai University, Taichung, Taiwan.
Wu CL; Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan. .; Department of post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan. .
Liu SF; Supervisor of Nursing Department, Taichung Veterans General Hospital, Taichung, Taiwan.
Lin PY; Department of Nursing, Taichung Veterans General Hospital, Taichung, Taiwan.
Cheng WL; Department of Computer Science, Tunghai University, Taichung, Taiwan.
Lin GY; Department of Nursing, Taichung Veterans General Hospital, Taichung, Taiwan.
Chung HC; Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.
Chen LC; College of Engineering, Tunghai University, Taichung, Taiwan.
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Źródło:
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Mar 18; Vol. 24 (1), pp. 77. Date of Electronic Publication: 2024 Mar 18.
Typ publikacji:
Journal Article
MeSH Terms:
Psychomotor Agitation*/diagnosis
Deep Learning*
Humans ; Artificial Intelligence ; Intensive Care Units ; Critical Care
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.
Autorzy:
Kim PJ; School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea.
Hwang HS; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Choi G; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sung HJ; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Ahn B; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Uh JS; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Yoon S; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Kim D; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Chun SM; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Jang SJ; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Go H; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 16; Vol. 14 (1), pp. 6366. Date of Electronic Publication: 2024 Mar 16.
Typ publikacji:
Journal Article
MeSH Terms:
Lung Neoplasms*/genetics
Lung Neoplasms*/surgery
Deep Learning*
Adenocarcinoma of Lung*/genetics
Adenocarcinoma of Lung*/surgery
Humans ; Neoplasm Recurrence, Local/pathology ; Risk Factors
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.
Autorzy:
Wang S; Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA.
Han J; Coriell Institute for Medical Research, Camden, NJ, USA.
Huang J; Shanghai Key Laboratory of Medical Epigenetics, Laboratory of Cancer Epigenetics, Institutes of Biomedical Sciences, Medical College of Fudan University, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China.
Islam K; Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA.
Shi Y; Shanghai Key Laboratory of Medical Epigenetics, Laboratory of Cancer Epigenetics, Institutes of Biomedical Sciences, Medical College of Fudan University, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China.
Zhou Y; Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Kim D; Coriell Institute for Medical Research, Camden, NJ, USA.
Zhou J; Health and Human Biology, Brown University, Providence, RI, USA.
Lian Z; Coriell Institute for Medical Research, Camden, NJ, USA.
Liu Y; Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA. .; Department of Bioengineering, Lehigh University, Bethlehem, PA, USA. .
Huang J; Coriell Institute for Medical Research, Camden, NJ, USA. .; Cooper Medical School of Rowan University, Camden, NJ, USA. .; Center for Metabolic Disease Research, Temple University Lewis Katz School of Medicine, Philadelphia, PA, USA. .
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Źródło:
Stem cell research & therapy [Stem Cell Res Ther] 2024 Mar 13; Vol. 15 (1), pp. 74. Date of Electronic Publication: 2024 Mar 13.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Animals ; Mice ; Hematopoietic Stem Cells/metabolism ; Hematopoiesis ; Multipotent Stem Cells ; Cell Differentiation
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.
Autorzy:
Wang Y; Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Gao R; Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Wei T; Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Johnston L; School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
Yuan X; Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Zhang Y; Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Yu Z; Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China. .; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China. .; School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China. .; Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .
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Corporate Authors:
Alzheimer’s Disease Neuroimaging Initiative
Źródło:
Journal of translational medicine [J Transl Med] 2024 Mar 11; Vol. 22 (1), pp. 265. Date of Electronic Publication: 2024 Mar 11.
Typ publikacji:
Multicenter Study; Journal Article
MeSH Terms:
Alzheimer Disease*/diagnostic imaging
Alzheimer Disease*/genetics
Deep Learning*
Cognitive Dysfunction*/diagnostic imaging
Cognitive Dysfunction*/genetics
Cognitive Dysfunction*/pathology
Humans ; Retrospective Studies ; Magnetic Resonance Imaging/methods ; Disease Progression
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.
Autorzy:
Grigo J; Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Szkitsak J; Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Höfler D; Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Fietkau R; Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Putz F; Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Bert C; Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany. .; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany. .
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Źródło:
Radiation oncology (London, England) [Radiat Oncol] 2024 Mar 08; Vol. 19 (1), pp. 33. Date of Electronic Publication: 2024 Mar 08.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Radiotherapy, Intensity-Modulated*/methods
Brain Neoplasms*/diagnostic imaging
Brain Neoplasms*/radiotherapy
Humans ; Feasibility Studies ; Retrospective Studies ; Radiotherapy Planning, Computer-Assisted/methods ; Radiotherapy Dosage ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.
Autorzy:
Huang J; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK. .; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK. .; Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK. .
Ferreira PF; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Wang L; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Department of Computing, Imperial College London, London, UK.
Wu Y; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Aviles-Rivero AI; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Schönlieb CB; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Scott AD; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Khalique Z; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Dwornik M; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Rajakulasingam R; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
De Silva R; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Pennell DJ; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Nielles-Vallespin S; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
Yang G; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK. .; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK. .; Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 07; Vol. 14 (1), pp. 5658. Date of Electronic Publication: 2024 Mar 07.
Typ publikacji:
Journal Article
MeSH Terms:
Diffusion Tensor Imaging*/methods
Deep Learning*
Algorithms ; Magnetic Resonance Imaging ; Magnetic Resonance Spectroscopy ; Diffusion Magnetic Resonance Imaging/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.
Autorzy:
Xia S; Institute of Medical Technology, Peking University Health Science Center, Haidian District, No. 38 Xueyuan Road, Beijing, 100191, China.; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Li Q; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Zhu HT; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Zhang XY; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Shi YJ; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Yang D; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Wu J; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Guan Z; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Lu Q; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Li XT; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
Sun YS; Institute of Medical Technology, Peking University Health Science Center, Haidian District, No. 38 Xueyuan Road, Beijing, 100191, China. .; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China. .
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Źródło:
BMC cancer [BMC Cancer] 2024 Mar 07; Vol. 24 (1), pp. 315. Date of Electronic Publication: 2024 Mar 07.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Rectal Neoplasms*/diagnostic imaging
Rectal Neoplasms*/therapy
Rectal Neoplasms*/pathology
Humans ; Neoadjuvant Therapy ; Retrospective Studies ; Semantics ; Magnetic Resonance Imaging/methods ; Image Processing, Computer-Assisted/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.
Autorzy:
Zhang X; Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
Yu X; Department of Computer Science and Technology, Zhejiang University, Hangzhou, P. R. China.
Liang W; Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
Zhang Z; School of Management, Hangzhou Dianzi University, Hangzhou, P. R. China.
Zhang S; Department of Computer Science and Technology, Zhejiang University, Hangzhou, P. R. China.
Xu L; Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
Zhang H; Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
Feng Z; Department of Computer Science and Technology, Zhejiang University, Hangzhou, P. R. China.
Song M; Department of Computer Science and Technology, Zhejiang University, Hangzhou, P. R. China.
Zhang J; Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
Feng S; Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
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Źródło:
Cancer medicine [Cancer Med] 2024 Mar; Vol. 13 (5), pp. e7104.
Typ publikacji:
Journal Article
MeSH Terms:
Carcinoma, Hepatocellular*/pathology
Liver Neoplasms*/pathology
Deep Learning*
Humans ; Artificial Intelligence ; Retrospective Studies ; Neoplasm Invasiveness
Czasopismo naukowe
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Tytuł:
Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.
Autorzy:
Wang R; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Chung CR; Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan.
Lee TY; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
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Źródło:
International journal of molecular sciences [Int J Mol Sci] 2024 Mar 01; Vol. 25 (5). Date of Electronic Publication: 2024 Mar 01.
Typ publikacji:
Journal Article
MeSH Terms:
RNA*/genetics
Deep Learning*
RNA Methylation ; Methylation ; Protein Processing, Post-Translational
Czasopismo naukowe
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Tytuł:
Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.
Autorzy:
Lee DK; Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Choi YJ; Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Lee SJ; Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Kang HG; Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea. .
Park YR; Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea. .
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 01; Vol. 14 (1), pp. 5079. Date of Electronic Publication: 2024 Mar 01.
Typ publikacji:
Journal Article
MeSH Terms:
Optic Disk*/diagnostic imaging
Optic Disk*/pathology
Deep Learning*
Optic Atrophy, Hereditary, Leber*/pathology
Optic Neuritis*/pathology
Humans ; Retrospective Studies ; Photography ; Atrophy/pathology
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan.
Autorzy:
Eisma JJ; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA.
McKnight CD; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
Hett K; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA.
Elenberger J; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA.
Han CJ; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA.
Song AK; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA.
Considine C; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA.
Claassen DO; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA.
Donahue MJ; Department of Neurology, Behavioral and Cognitive Neurology, Vanderbilt University Medical Center, 1500 21 stAve South, Village at Vanderbilt, Suite 2600, Nashville, TN, 37212, USA. .; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA. .; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA. .
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Źródło:
Fluids and barriers of the CNS [Fluids Barriers CNS] 2024 Feb 29; Vol. 21 (1), pp. 21. Date of Electronic Publication: 2024 Feb 29.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Adult ; Humans ; Young Adult ; Middle Aged ; Aged ; Aged, 80 and over ; Image Processing, Computer-Assisted/methods ; Longevity ; Choroid Plexus/diagnostic imaging ; Magnetic Resonance Imaging/methods
Czasopismo naukowe
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Tytuł:
An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.
Autorzy:
Chai H; School of Mathematics and Big Data, Foshan University, Foshan, 528000, China.
Lin S; School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510000, China.
Lin J; School of Mathematics and Big Data, Foshan University, Foshan, 528000, China.
He M; School of Mathematics and Big Data, Foshan University, Foshan, 528000, China.
Yang Y; School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510000, China.
OuYang Y; School of Mathematics and Big Data, Foshan University, Foshan, 528000, China. .
Zhao H; Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510000, China. .
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Źródło:
BMC bioinformatics [BMC Bioinformatics] 2024 Feb 29; Vol. 25 (1), pp. 88. Date of Electronic Publication: 2024 Feb 29.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Breast Neoplasms*/genetics
Humans ; Female ; Uncertainty ; Neural Networks, Computer ; Algorithms
Czasopismo naukowe
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Wyświetlanie 21-40 z 311284

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