NiftyNet: a deep-learning platform for medical imaging

Cited by (550)

Items 401–500 of 550

1. Towards efficient federated learning‐based scheme in medical cyber‐physical systems for distributed data
   Kehua Guo, Nan Li, Jian Kang, Jian Zhang · 2020 · Software Practice and Experience
2. CBRA: Cardiac biomarkers release analyzer
   Anna Procopio, Salvatore De Rosa, Francesco Montefusco, Giovanni Canino, Alessio Merola, Jolanda Sabatino, Jessica Ielapi, Ciro Indolfi, Francesco Amato, Carlo Cosentino · 2021 · Computer Methods and Programs in Biomedicine
3. Fast Infant MRI Skullstripping with Multiview 2D Convolutional Neural Networks
   Amod Jog, Patricia Ellen Grant, Joseph L. Jacobson, Andre J. W. van der Kouwe, Ernesta M. Meintjes, Bruce R. Fischl, Lilla Zöllei · 2019 · arXiv (Cornell University)
4. Medical Image Segmentation Methods: A Decision-Guided Survey Covering 2D/3D CNNs, Transformers, VLMs, SAM-Based Models and Diffusion Approaches
   Kadir Sabancı, Busra Aslan, Muhammet Fatih Aslan · 2026 · Bioengineering
5. DEEP LEARNING IN HEAT TRANSFER
   H.R. Tamaddon-Jahromi, Neeraj Kavan Chakshu, Hywel Thomas, Perumal Nithiarasu · 2022 · Annual Reviews of Heat Transfer
6. Enhancing Precision in Medical Imaging: A 3D CNN Approach for Fiducial Point Detection in MRI Data
   M. V. Suhas, Sanjib Sinha, Narayanan Mariyappa, H Anitha, Karunakar A. Kotegar · 2024 · IEEE Access
7. ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging
   Dimitrios Karkalousos, Ivana Išgum, Henk A. Marquering, Matthan W.A. Caan · 2024 · Computer Methods and Programs in Biomedicine
8. Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review
   Ramtin Babaeipour, Alexei V. Ouriadov, Matthew S. Fox · 2023 · Bioengineering
9. Distributed Deep Learning for Medical Image Processing in Cloud Environments
   Neeraj Varshney, Parul Madan, Anurag Shrivastava, C Praveen Kumar, A Kakoli Rao, Amit Srivastava · 2023 · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)
10. Generative Adversarial Networks for Lupus Diagnostics
    Pradeep Periasamy, Vetria L. Byrd · 2019 · Practice and Experience in Advanced Research Computing
11. Machine Learning Approaches to Rational Drug Design
    Salman Akhtar, Mohammad Kalim Ahmad Khan, Khwaja Osama · 2020 · Springer eBooks
12. Web Platform for Medical Deep Learning Services
    João C. Ferreira, Carlos M. A. Costa · 2021 · IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
13. Evaluating Interpretability in Deep Learning using Breast Cancer Histopathological Images
    Daniel C. Macedo, De Lima John W. S., Vinicius D. Santos, Moraes Tasso L. O., Neto Fernando M. P., Nicksson Arrais, Tiago da Silva Vinuto, Joao Lucena · 2022 · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
14. PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation
    Joshua Astley, Alberto M. Biancardi, Helen Marshall, Laurie Smith, Paul JC Hughes, Guilhem J. Collier, Laura C. Saunders, Graham Norquay, Malina-Maria Tofan, Matthew Q F Hatton et al. · 2023 · Scientific Reports
15. Data Augmentation Based on Substituting Regional MRIs Volume Scores
    Tuo Leng, Qingyu Zhao, Chao Yang, Zhufu Lu, Ehsan Adeli, Kilian M. Pohl · 2019 · Lecture notes in computer science
16. Comparative Analysis of Fuzzy C- Means and K-Means Clustering in the Case of Image Segmentation
    Arvind Kumar, Sartaj Singh Sodhi · 2021 · International Conference on Computing for Sustainable Global Development
17. Uncertainty-Aware Multi-resolution Whole-Body MR to CT Synthesis
    Kerstin Kläser, Pedro Borges, Richard Shaw, Marta Bianca Maria Ranzini, Marc Modat A, David Atkinson, Kris Thielemans, Brian F. Hutton, Vicky Goh, Gary Cook et al. · 2020 · Lecture notes in computer science
18. A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation
    Richard Shaw, Carole H. Sudre, Sebastien Ourselin A, M. Jorge Cardoso, Hugh Pemberton · 2021 · arXiv (Cornell University)
19. Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study
    Guorong Wang, B.T. Yang, Xiaoxia Qu, Jian Guo, Yongheng Luo, Xiao‐Quan Xu, Fei‐Yun Wu, Xiaoxue Fan, Yang Hou, Song Tian et al. · 2024 · Neuroradiology
20. Single annotator versus multi-annotator: Challenge of segmenting two neighboring hippocampus head and body with high precision
    H. Arabi, H. Zaidi · 2024 · Biomedical Signal Processing and Control
21. A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI
    M. Nisha, T Kannan, K. Sivasankari · 2024 · Mathematical Biosciences & Engineering
22. Classification of Brain MRI Images Using Convolution Neural Network and Transfer Learning
    N Ambily, K. Suresh · 2020 · International Conference on Computing, Communication and Networking Technologies (ICCCNT)
23. A Design Case Study of Artificial Intelligence Pipeline Visualization
    Yoonha Park, Yoonha Park, Jae Young Yun, Jae Young Yun · 2021 · Archives of Design Research
24. BMRMIA: A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System
    Yankun Cao, Lina Xu, Zhi Liu, Xiaoyan Xiao, Mingyu Wang, Qin Li, Hongji Xu, Geng Yang · 2022 · Computer Modeling in Engineering & Sciences
25. Deep integration model: A robust autonomous segmentation technique for hippocampus in MRI images of human head
    M. Nisha, T. Kannan, K. Sivasankari · 2022 · International Journal of Health Sciences
26. Opportunities and Challenges for Deep Learning in Brain Lesions
    Jay Patel, Ken Chang, Syed Rakin Ahmed, Ikbeom Jang, Jayashree Kalpathy–Cramer · 2022 · Lecture notes in computer science
27. Central Nervous System Tumors - Primary and Secondary
    Feyzi Birol Sarica · 2022 · IntechOpen eBooks
28. How the Oxynet web applications are used to crowdsource and interpret cardiopulmonary exercising tests data
    Andrea Zignoli, Alessandro Fornasiero, Federica Gilli, Barbara Pellegrini, Federico F. Schena · 2023 · Biomedical Signal Processing and Control
29. VGG16-PotatoGuard: A Deep Learning Approach to Detecting Leaf Diseases in Potatoes
    Tanvi Kaur Sasan, Jaspreet Kaur, Jasdeep Kaur, Jasneet Singh Chawla · 2024 · International Conference on Emerging Smart Computing and Informatics (ESCI)
30. Multiple representation contrastive self-supervised learning for pulmonary nodule detection✰
    Asghar Torki, Peyman Adibi, Hamidreza Baradaran Kashani · 2024 · Knowledge-Based Systems
31. Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning
    Risa Nakano, Hidetaka Arimura, Mohammad Haekal, Saiji Ohga · 2019 · International Forum on Medical Imaging in Asia (IFMIA)
32. Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data
    Rafeeq P H Ahmed, Pradeep Kumar Singh, Tanvir Ahmad · 2018 · EAI Endorsed Transactions on Energy Web
33. Balanced multi-image demons for non-rigid registration of magnetic resonance images
    Luca Mesin · 2020 · Magnetic Resonance Imaging
34. Research on Application of Ultrasound Medical Imaging Technology in Big Data Mining of Regional Medical Imaging
    Yubo Xie · 2020 · Journal of Medical Imaging and Health Informatics
35. Multi-modal brain tumor image segmentation based on improved U-net model
    Chong Ma, Xiaobo Li · 2021 · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)
36. RETRACTED ARTICLE: Deep multilayer and nonlinear Kernelized Lasso feature learning for healthcare in big data environment
    S. Prakash, K. Sangeetha · 2020 · Journal of Ambient Intelligence and Humanized Computing · Retracted
37. Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks
    Justin Lo, Jillian Cardinell, Alejo Costanzo, Dafna Sussman · 2022
38. Deep learning-based end-to-end scan-type classification, pre-processing, and segmentation of clinical neuro-oncology studies
    Satrajit Chakrabarty, Syed Amaan Abidi, Mina S. Mousa, Mahati Mokkarala, M. Kelsey, Pamela LaMontagne, Aristeidis Sotiras, Daniel S. Marcus · 2023 · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications
39. Regional deep atrophy: Using temporal information to automatically identify regions associated with Alzheimer’s disease progression from longitudinal MRI
    Mengjin Dong, Long Xie, Sandhitsu R. Das, Jiancong Wang, Laura E.M. Wisse, Robin de Florès, David A. Wolk, Paul A. Yushkevich · 2024 · Imaging Neuroscience
40. Assessing the feasibility of deep learning-based attenuation correction using photon emission data in 18F-FDG images for dedicated head and neck PET scanners
    Mahsa Shahrbabaki Mofrad, Ali Ghafari, Amin Amiri Tehranizadeh, Farahnaz Aghahosseini, Mohammadreza Ay, Saeed Farzenefar, Peyman Sheikhzadeh · 2025 · Biomedical Physics & Engineering Express
41. A platform for management and visualization of medical data and medical imaging
    Andrea Vázquez‐Ingelmo, Jesús Sampedro-Gómez, Antonio Sánchez-Puente, Víctor Vicente-Palacios, Pedro Ignacio Dorado-Díaz, Pedro Luis Sanchez, Francisco José García‐Peñalvo · 2020 · Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21)
42. Toward the Assessment of Intrinsic Geometry of Implicit Brain MRI Manifolds
    Karim Makki, D. Ben Salem, Boulbaba Ben Amor · 2021 · IEEE Access
43. Extracting Radiomic features from pre-operative and segmented MRI scans improved survival prognosis of glioblastoma Multiforme patients through machine learning: a retrospective study
    Gurinderjeet Kaur, Prashant Singh Rana, Vinay Arora · 2022 · Multimedia Tools and Applications
44. Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape
    Khamael Al-Dulaimi, Jasmine Banks, Aiman Al-Sabaawi, Kien Nguyen, Vinod Chandran, Inmaculada Tomeo-Reyes · 2023 · Sensors
45. A Robust Network for Multi-Label Abdominal Organs Segmentation
    Huu Sy Le, Kha Tu Huynh · 2024 · Vietnam Journal of Computer Science
46. Multimodal registration network with multi-scale feature-crossing
    Shuting Liu, Guoliang Wei, Yi Fan, Lei Chen, Zhaodong Zhang · 2024 · International Journal of Computer Assisted Radiology and Surgery
47. T1-Weighted Contrast-Enhanced Synthesis for Multi-Contrast MRI Segmentation
    Muzaffer Özbey, Tolga Çukur · 2020 · Signal Processing and Communications Applications Conference (SIU)
48. Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images
    Alexander Mulet de los Reyes, María Elena Buemi, Maikel Noriega Alemán, Cecilia Ana Suárez · 2018 · Congreso Argentino de Ciencias de la Informática y Desarrollos de Investigación (CACIDI)
49. Retracted: Design of Deep Learning Approach for Expression of Genes by Tumour RNA-Sequencing Data
    Pravin V. Shinde, Rajesh Deshmukh, Preeti Patil · 2022 · IEEE International Conference for Convergence in Technology (I2CT) · Retracted
50. Knowledge Graph Modeling in Healthcare: A Bibliometric Analysis
    Muhammad Ariful Furqon, Nina Fadilah Najwa, Deny Hermansyah, Mohammad Zarkasi · 2022 · Jurnal Komputer Terapan
51. “Deep learning” for healthcare: Opportunities, threats, and challenges
    Russell Kabir, Madhini Sivasubramanian, Geeta Hitch, Saira Hakkim, John Kainesie, Divya Vinnakota, Ilias Mahmud, Ehsanul Hoque Apu, Haniya Zehra Syed, Ali Davod Parsa · 2023 · Elsevier eBooks
52. Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures
    Andrés Larroza, L. Moliner, Juan M. Alvarez-Gomez, S. Oliver, Héctor Espinós-Morató, Marina Vergara, María José Rodríguez-Álvarez · 2019 · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
53. A Deep Learning Network for Coarse-to-Fine Deformable Medical Image Registration
    Lintao Zhang, Shunbo Hu, Guoqiang Li, Mingtao Liu, Yongfang Wang, Deqian Fu, Wenyin Zhang · 2020 · Communications in computer and information science
54. Künstliche Intelligenz in der Medizin und Gynäkologie – Holzweg oder Heilversprechen?
    Daniel Sonntag · 2021 · Der Gynäkologe
55. Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
    Reuben Dorent, Wenqi Li, Jinendra Ekanayake, Sebastien Ourselin A, Tom K. Vercauteren · 2019 · Research Portal (King's College London)
56. Eisen: a python package for solid deep learning
    Frank Mancolo · 2020 · arXiv (Cornell University)
57. MeDaS: An open-source platform as service to help break the walls between medicine and informatics
    Liang Zhang, Johann Li, Ping Li, Xiaoyuan Lu, Peiyi Shen, Guangming Zhu, Syed Afaq Ali Shah, Bennarmoun, Mohammed, Kun Qian, Björn Wolfgang Schuller · 2020 · arXiv (Cornell University)
58. Portable framework to deploy deep learning segmentation models for medical images
    Aditi Iyer, Eve LoCastro, Aditya Apte, Harini Veeraraghavan, Joseph O. Deasy · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
59. Quantitative Parametric Mapping of Tissues Properties from Standard Magnetic Resonance Imaging Enabled by Deep Learning
    Yan Wu, Yajun Ma, Youngwook Kee, Nataliya Kovalchuk, Dante P. I. Capaldi, Hongyi Ren, Hancock, Steven, Eric Yao Chang, Marcus T. Alley, John M. Pauly et al. · 2021 · arXiv (Cornell University)
60. Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact
    Jan Egger, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jianning Li, Roman Kern · 2021 · PeerJ Computer Science
61. Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data
    Ying‐Hwey Nai, Haohui Liu, Anthonin Reilhac · 2022 · Physica Medica
62. Integrated clinicopathological–radiomic–blood model for glioma survival prediction via machine learning: a multicenter cohort study
    Zhihao Wang, Tao Chang, Jing Yang, Chaodong Xiang, Xianqi Wang, Pinzhen Chen, Yunhui Zeng, Lanqin Deng, Wenhao Li, Yuhang Ou et al. · 2025 · Neurosurgical Review
63. Ultrasound in medicine from 2014 to 2024: A bibliometric review
    Zhao Chenhao, Zhou Ruqi, Sun Xiaozhuo, Wang Jiabo, Liu Jiao, Mu Yajuan · 2026 · Medicine
64. Artificial intelligence in carotid research: a 25-year bibliometric analysis of global trends and future directions
    Shi-Chao Zhu, Jiaxin Dong, Yudan Wu, Yue Gao, Rui Gao, Yong-Hui Yuan, Shuang Wang, Guang-Wei Zhang · 2026 · Frontiers in Artificial Intelligence
65. Cross Modality Alignment of Medical Volumes using Spatio-Semantic Attentive Cycle-GAN
    Xiaohui S. Lin, Yi Xu, Mingda Wang, Bingbing Ni, Xiaokang Yang, Guangyu Tao, Xiaodan Ye · 2019 · IEEE International Conference on Multimedia and Expo (ICME)
66. A Modified Deep Convolutional Neural Network for Brian Abnormalities Detection
    Aneesh S Perumprath, Raikar Ankit Arun, Vishnu Raj · 2020 · International Journal of Engineering Research and
67. Radiomics and Radiogenomics with Deep Learning in Neuro-oncology
    Jay Patel, Mishka Gidwani, Ken Chang, Jayashree Kalpathy–Cramer · 2020 · Lecture notes in computer science
68. In-house development, implementation and evaluation of machine learning software for automated clinical scan processing
    Jonathan Christopher Taylor, Michael J. Sharkey, Peter Metherall · 2021 · Nuclear Medicine Communications
69. Improving Similarity Metric of Multi-modal MR Brain Image Registration Via a Deep Ensemble
    Natan Andrade, Fabio Augusto Faria, Fábio A. M. Cappabianco · 2021 · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
70. Transformation-consistent semi-supervised learning for prostate CT radiotherapy
    Yichao Li, Mohamed S. Elmahdy, Michael S. Lew, Marius Staring · 2022 · SPIE Medical Imaging: Computer-Aided Diagnosis
71. Voting Ensemble SVM Model for Deep CNN Based Breast Histopathology Classification
    Jyoti Chowdhary, Praveen Sankaran · 2022 · IEEE India Conference
72. An Active Learning Approach to Deep Learning Glioma Segmentation from Brain MR Images
    Andrew S. Boehringer, Amirhossein Sanaat, Hossein Arabi, Habib Zaidi · 2022 · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
73. Prenatal Detection of Ventricular Septal Defects by VGG -16 Model Using Ultrasound Images
    Kavitha. D, Lakshmi.C. R · 2023 · International Conference on Mobile Networks and Wireless Communications (ICMNWC)
74. Deep-learning-based Attenuation Correction for 68Ga-DOTATATE Whole-body PET Imaging: A Dual-center Clinical Study
    Mahsa Sobhi Lord, Jalil Pirayesh Islamian, Negisa Seyyedi, Rezvan Samimi, Saeed Farzanehfar, Mahsa Shahrbabki, Peyman Sheikhzadeh · 2024 · Molecular Imaging and Radionuclide Therapy
75. Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation
    Yiming Li, Hanchi Ren, Jingjing Deng, Xiaoke Ma, Xianghua Xie · 2024 · IEEE International Symposium on Biomedical Imaging (ISBI)
76. Brain tumour detection using deep learning technique
    R. Meena, Shaik Sharmila, E. Sheela, K. Thamizharasi · 2024 · i-manager’s Journal on Pattern Recognition
77. Automated Mouse Organ Segmentation
    Naveen Ashish, Mi‐Youn Brusniak · 2018 · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)
78. Methoden der künstlichen Intelligenz in der vaskulären Medizin
    Lukas Bruder, Benedikt Reutersberg, Marina Bassilious, W. Schüttler, Hans‐Henning Eckstein, Michael W. Gee · 2019 · Gefässchirurgie
79. YÜZ TANIMA SİSTEMLERİNDE DOĞRULUK PERFORMANSLARININ DEĞERLENDİRİLMESİ
    Bekir Aksoy · 2019 · Mühendislik Bilimleri ve Tasarım Dergisi
80. Evaluation of a deep learning based brain tumour segmentation method
    Nor Kharul Aina Mat Din, Ashrani Aizzuddin Abd. Rahni · 2020 · Journal of Physics Conference Series
81. Applications of Computational Intelligence Techniques in Chemical and Biochemical Analysis
    Miles Gibson, Benita Claire Percival, Martin C. Grootveld, Katy Woodason, Justine Leenders, Kingsley Nwosu, Shina Caroline Lynn Kamerlin, Philippe Wilson · 2020 · Royal Society of Chemistry eBooks
82. Multistage Classification and Segmentation of Brain MR Image Using Modified Soft Computing Techniques
    G. Sethuram Rao, D. Vydeki · 2022 · IntechOpen eBooks
83. Retracted: 3D U-NET Convolutional Neural Network for Segmentation of Brain Tumour Tissues in Hgg and Lgg Magnetic Resonance Imageing
    Arpit Mohan, Deependra Rastogi, Kartik Rajput · 2022 · International Conference on Computing Communication Control and automation · Retracted
84. Library of model implementations for sharing deep-learning image segmentation and outcomes models
    Aditya Apte, Aditi Iyer, Maria Thor, Rutu Pandya, Rabia Haq, Amita Shukla‐Dave, Hu Yu-Chi, Sharif Elguindi, Harini Veeraraghavan, Jung Hun Oh et al. · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
85. Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations.
    Ayman Al‐Kababji, Fayçal Bensaali, Sarada Prasad Dakua · 2021 · arXiv (Cornell University)
86. Estimating MRI Image Quality via Image Reconstruction Uncertainty
    Richard Shaw, Carole H. Sudre, Sebastien Ourselin A, M. Jorge Cardoso · 2021 · arXiv (Cornell University)
87. Medical Image to Image Translation using Multi Label Activated Gradients for General Adversial Networks ((MLAG GAN))
    M. Raja, S. Vijayachitra · 2022 · Research Square
88. Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks
    Justin Lo, Jillian Cardinell, Alejo Costanzo, Dafna Sussman · 2022
89. Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images
    Subhradeep Kayal, Florian Dubost, Harm A.W.M. Tiddens, Marleen de Bruijne · 2020 · IEEE International Symposium on Biomedical Imaging (ISBI)
90. Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
    Ester Bonmati, Yipeng Hu, Hans Peter Dietz, Dean C. Barratt, Tom K. Vercauteren, Nikhil Sindhwani, Jan D’hooge, Jan A. Deprest · 2018 · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling
91. PIMMS: Permutation Invariant Multi-Modal Segmentation
    Thomas Varsavsky, Zach Eaton-Rosen, Carole H. Sudre, Parashkev Nachev, M. Jorge Cardoso · 2018 · arXiv (Cornell University)
92. Convolution Neural Network Application in Kidney Tumor Segmentation on CT Images
    Jianping Hunag, Zefang Lin · 2019 · Kidney Tumor Segmentation Challenge (KiTS)
93. Organ At Risk Segmentation with Multiple Modality
    Kuan-Lun Tseng, Winston H. Hsu, Chun-Ting Wu, Ya-Fang Shih, Fan-Yun Sun · 2019 · arXiv (Cornell University)
94. Creational and Structural Patterns in a Flexible Machine Learning Framework for Medical Ultrasound Diagnostics
    Corey M. Thibeault · 2019 · EPiC series in computing
95. АЛГОРИТМ СЕГМЕНТАЦІЇ НОВОУТВОРЕНИХ ПУХЛИН НА МРТ ЗОБРАЖЕННІ ГОЛОВНОГО МОЗКУ ЗА ДОПОМОГОЮ КОМБІНАЦІЙ НЕЙРОННИХ МЕРЕЖ
    Костянтин Віталійович Помпа, Віталій Борисович Максименко · 2019 · Біомедична інженерія і технологія
96. Towards a Domain-Specific Deep Learning Models for Medical Image Analysis
    Nkechinyere Joy, Olawuyi, Babajide Samuel Afolabi, Babajide Samuel Afolabi, Bola Ibitoye · 2020 · International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS)
97. Deep learning analysis of eye fundus images to support medical diagnosis
    Perdomo Charry, Óscar Julián · 2020
98. An Analysis of Multi-organ Segmentation Performance of CNNs on Abdominal Organs with an Emphasis on Kidney
    Mahmud Elahi Akhter, Ashfia Binte Habib, Rishad Arfin, Fahimul Haque, Syed Athar Bin Amir, Zunayeed Bin Zahir, Md. Shahriar Hussain, Rajesh Palit · 2020 · Lecture notes in electrical engineering
99. A 2D ResU-Net Powered Segmentation of Thoracic Organs at Risk Using Computed Tomography Images
    Mohit Asudani, Alarsh Tiwari, Harsh Kataria, Vipul Kumar Mishra, Anurag Goswami · 2021 · Communications in computer and information science
100. State-of-the-art equipment for rapid and accurate diagnosis of COVID-19
     Navchetan Awasthi, Swati B. Gupta, Amritanjali Kiran, Rohit Pardasani · 2021 · Elsevier eBooks