Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans

Cited by (746)

Items 101–200 of 746

1. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence
   Yaqiong Zhang, Fengxia Li, Fuqiang Yuan, Kai Zhang, Lijuan Huo, Zichen Dong, Yiming Lang, Yapeng Zhang, Meihong Wang, Zenghui Gao et al. · 2020 · Digestive and Liver Disease
2. Identity Threats as a Reason for Resistance to Artificial Intelligence: Survey Study With Medical Students and Professionals
   Jussupow, Ekaterina, Spohrer, Kai, Heinzl, Armin · 2022 · JMIR Formative Research
3. Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm
   Kaijian Xia, Hongsheng Yin, Yudong Zhang · 2018 · Journal of Medical Systems
4. Ensemble Knowledge Transfer for Semantic Segmentation
   Ishan Nigam, Chen Huang, Deva Ramanan · 2018 · IEEE Winter Conference on Applications of Computer Vision
5. Deep learning modeling using normal mammograms for predicting breast cancer risk
   Dooman Arefan, Aly A. Mohamed, Wendie A. Berg, Margarita L. Zuley, Jules H. Sumkin, Shandong Wu · 2019 · Medical Physics
6. Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study
   Faezehsadat Shahidi, Salwani Mohd Daud, Hafiza Abas, Noor Azurati Ahmad, Nurazean Maarop · 2020 · IEEE Access
7. A deep learning, image based approach for automated diagnosis for inflammatory skin diseases
   Haijing Wu, Heng Yin, Haipeng Chen, Moyuan Sun, Xiaoqing Liu, Yizhou Yu, Yang Tang, Hai Long, Bo Zhang, Jing Zhang et al. · 2020 · Annals of Translational Medicine
8. Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis
   Jiamin Liang, Xin Yang, Yuhao Huang, Haoming Li, Shuangchi He, Xindi C. Hu, Zejian Chen, Wufeng Xue, Jun Cheng, Dong Ni · 2022 · Medical Image Analysis
9. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification
   Prabhpreet Kaur, Gurvinder Singh, Parminder Kaur · 2019 · Informatics in Medicine Unlocked
10. Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization
    Liyong Ma, Chengkuan Ma, Yuejun Liu, Xuguang Wang · 2019 · Computational Intelligence and Neuroscience
11. Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network
    Majid A. Nawaz, Ahmed Adel, Taysir Hassan · 2018 · International Journal of Advanced Computer Science and Applications
12. RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning
    Kenneth A. Philbrick, Alexander D. Weston, Zeynettin Akkus, Timothy Lee Kline, Panagiotis D. Korfiatis, Tomas Sakinis, Petro Kostandy, Arunnit Boonrod, Atefeh Zeinoddini, Naoki Takahashi et al. · 2019 · Journal of Imaging Informatics in Medicine
13. Predicting post-stroke pneumonia using deep neural network approaches
    Yanqiu Ge, Qinghua Wang, Li Wang, Honghu Wu, Peng Chen, Jiajing Wang, Yuan Xu, Gang Xiong, Yaoyun Zhang, Yingping Yi · 2019 · International Journal of Medical Informatics
14. DeTrac: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks
    Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber · 2020 · IEEE Access
15. A Tour of Unsupervised Deep Learning for Medical Image Analysis
    Khalid Raza, Nripendra Kumar Singh · 2021 · Current Medical Imaging Formerly Current Medical Imaging Reviews
16. Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making
    Ciritsis, Alexander, Rossi, Cristina, Eberhard, Matthias, Marcon, Magda, Becker, Anton S., Boss, Andreas · 2019 · European Radiology
17. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks
    Jin Woo Choi, Yunseo Ku, Byeong Wook Yoo, Jung Ah Kim, Dong Soon Lee, Young Jun Chai, Hyoun‐Joong Kong, Hee Chan Kim · 2017 · PLoS ONE
18. Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy
    Price A Jackson, Nicholas Hardcastle, Noel Dawe, Tomas Kron, Michael S. Hofman, Rodney J. Hicks · 2018 · Frontiers in Oncology
19. Machine learning in cardiac CT: Basic concepts and contemporary data
    Gurpreet Singh, Subhi Jamal Alaref, Marly van Assen, Timothy Suyong Kim, Alexander van Rosendael, Kranthi K. Kolli, Aeshita Dwivedi, Gabriel Maliakal, Mohit Kumar Pandey, Jing Wang et al. · 2018 · Journal of cardiovascular computed tomography
20. Stan: Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
    Bryar Shareef, Min Xian, Aleksandar Vakanski · 2020 · IEEE International Symposium on Biomedical Imaging (ISBI)
21. Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy
    Shudong Wang, Li-Yuan Dong, Xun Wang, Xingguang Wang · 2020 · Open Medicine
22. WGAN-Based Synthetic Minority Over-Sampling Technique: Improving Semantic Fine-Grained Classification for Lung Nodules in CT Images
    Qingfeng Wang, Xuehai Zhou, Chao Wang, Zhiqin Liu, Jun Huang, Ying Zhou, Changlong Li, Hang Zhuang, Jie‐Zhi Cheng · 2019 · IEEE Access
23. Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning
    Joshua Levy, A. James O’Malley · 2020 · BMC Medical Research Methodology
24. COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays
    Rajeev Kumar Singh, Rohan Pandey, Rishie Nandhan Babu · 2021 · Neural Computing and Applications
25. Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography
    Choi, Ji Soo, Han, Boo-Kyung, Ko, Eun Sook, Bae, Jung Min, Ko, Eun Young, Song, So Hee, Kwon, Mi-ri, Shin, Jung Hee, Hahn, Soo Yeon · 2019 · Korean Journal of Radiology
26. Deep and joint learning of longitudinal data for Alzheimer's disease prediction
    Baiying Lei, Mengya Yang, Peng Yang, Feng Zhou, Hou Wen, Wenbin Zou, Xia Li, Tianfu Wang, Xiaohua Xiao, Shuqiang Wang · 2020 · Pattern Recognition
27. Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer
    Sheng Weng, Xiaoyun Xu, Jiasong Li, Stephen T. C. Wong · 2017 · Journal of Biomedical Optics
28. Applications of deep learning for the analysis of medical data
    Hyun‐Jong Jang, Kyung‐Ok Cho · 2019 · Archives of Pharmacal Research
29. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma
    Si Eun Lee, Kyunghwa Han, Jin Young Kwak, Eunjung Lee, Eun‐Kyung Kim · 2018 · Scientific Reports
30. Computational biology: deep learning
    William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts · 2017 · Emerging Topics in Life Sciences
31. A holistic overview of deep learning approach in medical imaging
    Rammah Yousef, Gaurav Gupta, Nabhan Yousef, Manju Khari · 2022 · Multimedia Systems
32. Pre-trained deep learning models for brain MRI image classification
    Srigiri Krishnapriya, Yepuganti Karuna · 2023 · Frontiers in Human Neuroscience
33. Lung nodules diagnosis based on evolutionary convolutional neural network
    Giovanni L. F. da Silva, Otilio Paulo da Silva Neto, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass · 2017 · Multimedia Tools and Applications
34. BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
    Yingtao Zhang, Min Xian, Heng-Da Cheng, Bryar Shareef, Jianrui Ding, Fei Xu, Kuan Huang, Boyu Zhang, Chunping Ning, Ying Wang · 2022 · Healthcare
35. A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis
    M. F. Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Ashfia Jannat Keya, Abu Quwsar Ohi, Md. Rashedul Islam, Jong-Myon Kim · 2021 · Cancers
36. Ensemble Learners of Multiple Deep CNNs for Pulmonary Nodules Classification Using CT Images
    Baihua Zhang, Shouliang Qi, Patrice Monkam, Chen Li, Fan Yang, Yudong Yao, Wei Qian · 2019 · IEEE Access
37. Impact of quality, type and volume of data used by deep learning models in the analysis of medical images
    Andreea Roxana Luca, Tudor Florin Ursuleanu, Liliana Gheorghe, Roxana Grigorovici, Stefan Iancu, Maria Hlusneac, Alexandru Grigorovici · 2022 · Informatics in Medicine Unlocked
38. Reinventing polysomnography in the age of precision medicine
    Diane C Lim, Diego R. Mazzotti, Kate Sutherland, Jesse W. Mindel, Jinyoung Kim, Peter A. Cistulli, Ulysses J. Magalang, Allan I Pack, Philip de Chazal, Thomas Penzel · 2020 · Sleep Medicine Reviews
39. Deep Learning Application Pros And Cons Over Algorithm
    Ata Jahangir Moshayedi, Atanu Shuvam Roy, Amin Kolahdooz, Yang Shuxin · 2022 · EAI Endorsed Transactions on AI and Robotics
40. Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection
    Bum-Chae Kim, Jee Seok Yoon, Jun-Sik Choi, Heung‐Il Suk · 2019 · Neural Networks
41. Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study
    Xiaoyan Wang, Fangqin Fei, Jiawen Wei, Mingxue Huang, Fengling Xiang, Jing Tu, Yaping Wang, Jinhua Gan · 2024 · Frontiers in Public Health
42. Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review
    Henning Müller, Devrim Ünay · 2017 · IEEE Transactions on Multimedia
43. A comprehensive review on brain tumor segmentation and classification of MRI images
    Champakamala Sundar Rao, K. Karunakara · 2021 · Multimedia Tools and Applications
44. Pap smear image classification using convolutional neural network
    Kangkana Bora, Manish Chowdhury, Lipi B. Mahanta, Malay K. Kundu, Anup K. Das · 2016 · Indian Conference on Computer Vision, Graphics and Image Processing
45. Descriptive analysis of dental X-ray images using various practical methods: A review
    Anuj Kumar, H.S. Bhadauria, Annapurna Singh · 2021 · PeerJ Computer Science
46. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends
    Kamlesh Kumar, Prince Kumar, Dipankar Deb, Mihaela-Ligia Ungureșan, Vlad Mureşan · 2023 · Healthcare
47. Multi-objective differential evolution based random forest for e-health applications
    Manjit Kaur, Hemant Kumar Gianey, Dilbag Singh, Munish Sabharwal · 2019 · Modern Physics Letters B
48. Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT
    Johanna Uthoff, Matthew J. Stephens, John D. Newell, Eric A. Hoffman, Jared Larson, Nicholas Koehn, Frank A. De Stefano, Chrissy M. Lusk, Angela S. Wenzlaff, Donovan Watza et al. · 2019 · Medical Physics
49. Breast Cancer Image Classification via Multi-Network Features and Dual-Network Orthogonal Low-Rank Learning
    Yongjun Wang, Baiying Lei, Ahmed Elazab, Ee-Leng Tan, Wei Wang, Fanglin Huang, Xuehao Gong, Tianfu Wang · 2020 · IEEE Access
50. Synthetic Medical Images Using F&BGAN for Improved Lung Nodules Classification by Multi-Scale VGG16
    Defang Zhao, Dandan Zhu, Jianwei Lu, Ye Luo, Guokai Zhang · 2018 · Symmetry
51. A novel soft attention-based multi-modal deep learning framework for multi-label skin lesion classification
    Aslı Nur Ömeroğlu, Hussein M.A. Mohammed, Emin Argun Oral, Serdar Aydın · 2023 · Engineering Applications of Artificial Intelligence
52. A Deep Learning Based Alternative to Beamforming Ultrasound Images
    Arun Asokan Nair, Trac Duy Tran, Austin Reiter, Muyinatu A. Lediju Bell · 2018 · IEEE International Conference on Acoustics Speech and Signal Processing
53. Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study
    David S. Gierada, William C. Black, Caroline Chiles, Paul F. Pinsky, David F. Yankelevitz · 2020 · Radiology Imaging Cancer
54. Deep learning radiomics in breast cancer with different modalities: Overview and future
    Ting Pang, Jeannie Hsiu Ding Wong, Wei Lin Ng, Chee Seng Chan · 2020 · Expert Systems with Applications
55. Lung Nodule Classification on Computed Tomography Images Using Deep Learning
    Amrita Naik, Damodar Reddy Edla · 2020 · Wireless Personal Communications
56. Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images
    Floris Heutink, Valentin Koch, Berit M. Verbist, Willem-Jan van der Woude, Emmanuel A. M. Mylanus, Wendy J. Huinck, Ioannis Sechopoulos, Marco Caballo · 2020 · Computer Methods and Programs in Biomedicine
57. Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data
    Bens Pardamean, Tjeng Wawan Cenggoro, Reza Rahutomo, Arif Budiarto, Ettikan Kandasamy Karuppiah · 2018 · Procedia Computer Science
58. Prospects of deep learning for medical imaging
    Jonghoon Kim, Jisu Hong, Hyunjin Park · 2018 · Precision and Future Medicine
59. Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification
    Pooyan Sedigh, Rasoul Sadeghian, Mehdi Tale Masouleh · 2019
60. CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks
    Sourabh Shastri, Isha Kansal, Sachin Kumar, Kuljeet Singh, Renu Popli, Vibhakar Mansotra · 2022 · Health and Technology
61. Breast cancer classification using deep learned features boosted with handcrafted features
    Unaiza Sajid, Rizwan Ahmed Khan, Shahid Munir Shah, Sheeraz Arif · 2023 · Biomedical Signal Processing and Control
62. Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
    Stojan Trajanovski, Dimitrios Mavroeidis, Christine Leon Swisher, Binyam Gebrekidan Gebre, Bastiaan S. Veeling, Rafael Wiemker, Tobias Klinder, Amir Tahmasebi, Shawn M. Regis, Christoph Wald et al. · 2021 · Computerized Medical Imaging and Graphics
63. COVID-19 classification using deep feature concatenation technique
    Waleed Saad, Wafaa A. Shalaby, Mona Shokair, Fathi E. Abd El‐Samie, Moawad Ibrahim Dessouky, Essam Abdellatef · 2021 · Journal of Ambient Intelligence and Humanized Computing
64. Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network
    Antônio Oseas de Carvalho Filho, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass · 2018 · Pattern Recognition
65. Values and Ethics in Information Systems
    Sarah Spiekermann, Hanna Krasnova, Oliver Fast Hinz, Annika Baumann, Alexander Benlian, Henner Gimpel, Irina Heimbach, Antonia Köster, Alexander Maedche, Björn Niehaves et al. · 2022 · Business & Information Systems Engineering
66. Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction
    Sharbel Adib Elhage, Eva Barbara Deerenberg, Sullivan Armando Ayuso, Keith Joseph Murphy, Jenny Meng Shao, Kent Williams Kercher, Neil James Smart, John Patrick Fischer, Vedra Abdomerovic Augenstein, Paul Dominick Colavita et al. · 2021 · JAMA Surgery
67. Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis
    Ki-Wook Kim, Sung‐Won Kim, Seung Hyun Lee, Seung Hyun Lee, Hye Sun Lee, Sung Jun Kim · 2018 · Scientific Reports
68. A new modular neural network approach with fuzzy response integration for lung disease classification based on multiple objective feature optimization in chest X-ray images
    Sergio Varela-Santos, Patricia Melín · 2020 · Expert Systems with Applications
69. Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning
    Emilio Garcia, Renato Hermoza, César Beltrán, Luis Cano, Miluska Castillo, Carlos Castaneda · 2017 · Proceedings - IEEE Symposium on Computer-Based Medical Systems
70. The seven key challenges for the future of computer-aided diagnosis in medicine
    Juri Yanase, Evangelos Triantaphyllou · 2019 · International Journal of Medical Informatics
71. Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database
    Ke Yan, Xiaosong Wang, Le Lü, Ling Zhang, Adam P. Harrison, Mohammadhadi H. Bagheri, Ronald M. Summers · 2019 · Advances in computer vision and pattern recognition
72. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification
    Prabhpreet Kaur, Gurvinder Singh, Parminder Kaur · 2019 · Informatics in Medicine Unlocked
73. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer
    Lei Cong, Wanbing Feng, Zhigang Yao, Xiaoming Zhou, Wei Lie Xiao · 2020 · Journal of Cancer
74. Real-world big-data studies in laboratory medicine: Current status, application, and future considerations
    Chaochao Ma, Xinlu Wang, Jie Wu, Xinqi Cheng, Liangyu Xia, Fang Xue, Ling Qiu · 2020 · Clinical Biochemistry
75. Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks
    Shao-Lun Lu, Furen Xiao, Jason Chia‐Hsien Cheng, Wen-Chi Yang, Yueh-Hung Cheng, Yu‐Cheng Chang, Jhih-Yuan Lin, Chih-Hung Liang, Jen-Tang Lu, Ya‐Fang Chen et al. · 2021 · Neuro-Oncology
76. Deep learning LI-RADS grading system based on contrast enhanced multiphase MRI for differentiation between LR-3 and LR-4/LR-5 liver tumors
    Yunan Wu, Gregory M. White, Tyler Cornelius, Indraneel Gowdar, Mohammad H. Ansari, Mark P. Supanich, Jie Deng · 2020 · Annals of Translational Medicine
77. Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning
    Xinzhuo Zhao, Shouliang Qi, Baihua Zhang, He Ma, Wei Qian, Yudong Yao, Jianjun Sun · 2019 · Journal of X-Ray Science and Technology
78. Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology
    Ke Yan, Yifan Peng, Veit Sandfort, Mohammadhadi H. Bagheri, Zhiyong Lu, Ronald M. Summers · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
79. Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies
    Andrik Rampun, Bryan W. Scotney, Philip Morrow, Hui Wang, John A. Winder · 2018 · Journal of Imaging
80. Semantic segmentation of breast ultrasound image with fuzzy deep learning network and breast anatomy constraints
    Kuan Huang, Yingtao Zhang, He Cheng, Ping Xing, Boyu Zhang · 2021 · Neurocomputing
81. Machine Learning in Nuclear Medicine: Part 1—Introduction
    Carlos Uribe, Sulantha Sanjeewa Mathotaarachchi, Vincent Gaudet, Kenneth Carless Smith, Pedro Rosa‐Neto, François Bénard, Sandra E. Black, Katherine Zukotynski · 2019 · Journal of Nuclear Medicine
82. Efficient Quantum Image Classification Using Single Qubit Encoding
    Philip Easom, Ahmed Bouridane, Ammar Belatreche, Richard Jiang, Somaya Ali Al-Maadeed · 2022 · IEEE Transactions on Neural Networks and Learning Systems
83. Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images
    Antonio Brunetti, Leonarda Carnimeo, Gianpaolo Francesco Trotta, Vitoantonio Bevilacqua · 2018 · Neurocomputing
84. Machine learning for radiomics-based multimodality and multiparametric modeling
    Lise Wei, Sarah O.S. Osman, Mathieu Hatt, Issam El Naqa · 2019 · The Quarterly Journal of Nuclear Medicine and Molecular Imaging
85. Efficient Deep Learning Approaches for Health Informatics
    T M Navamani · 2019 · Elsevier eBooks
86. Deep feature extraction and classification of breast ultrasound images
    Kriti, J. Virmani, R. Agarwal · 2020 · Multimedia tools and applications
87. Lung cancer detection and classification with DGMM-RBCNN technique
    Sanjukta Rani Jena, Selvaraj Thomas George, D. Narain Ponraj · 2021 · Neural Computing and Applications
88. PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma
    Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O. Deasy · 2021 · Bioinformatics
89. Dimensionality reduction of mass spectrometry imaging data using autoencoders
    Spencer A. Thomas, Alan Race, Rory T. Steven, Ian S. Gilmore, Josephine Bunch · 2016 · IEEE Symposium Series on Computational Intelligence (SSCI)
90. Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective
    Aly A. Mohamed, Yahong Luo, Hong Peng, Rachel Catherine Jankowitz, Shandong Wu · 2017 · Journal of Imaging Informatics in Medicine
91. BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis
    Erlei Zhang, Stephen J. Seiler, Mingli Chen, Weiguo Lu, Xuejun Gu · 2020 · Physics in Medicine and Biology
92. A vehicle detection and tracking method for traffic video based on faster R-CNN
    Mohamed Othmani · 2022 · Multimedia Tools and Applications
93. Prediction of reader estimates of mammographic density using convolutional neural networks
    Georgia V. Ionescu, Martin Fergie, Michael Berks, Elaine F. Harkness, Johan Hulleman, Adam R. Brentnall, Jack M. Cuzick, D. Gareth Evans, Susan Astley · 2019 · Journal of medical imaging
94. Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network
    Chia‐Hung Chen, Yan‐Wei Lee, Yao‐Sian Huang, Wei-Ren Lan, Ruey‐Feng Chang, Chih‐Yen Tu, Chih‐Yu Chen, Wei-Chih Liao · 2019 · Computer Methods and Programs in Biomedicine
95. Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks
    Yohan Sumathipala, Nathan S. Lay, Barış Türkbey, Clayton Smith, Peter L. Choyke, Ronald M. Summers · 2018 · Journal of medical imaging
96. Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis
    Jing Gong, Jiyu Liu, Hao Wen, Shengdong Nie, Shengping Wang, Weijun Peng · 2019 · Physics in Medicine and Biology
97. Joint DBN and Fuzzy C-Means unsupervised deep clustering for lung cancer patient stratification
    Zijuan Zhao, Juanjuan Zhao, Kai Song, Akbar Hussain, Qianqian Du, Yunyun Dong, Jihua Liu, Xiaotang Yang · 2020 · Engineering Applications of Artificial Intelligence
98. Deep learning in breast radiology: current progress and future directions
    William C. Ou, Dogan S. Polat, Başak E. Doğan · 2021 · European Radiology
99. The utilisation of convolutional neural networks in detecting pulmonary nodules: a review
    Andrew Murphy, Matt R. Skalski, Frank Gaillard · 2018 · British Journal of Radiology
100. High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains
     Dong‐Hoon Lee, Sunghoon Choi, Hee‐Joung Kim · 2018 · Medical Physics