The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Cited by (1,012)

Items 101–200 of 1,012

1. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning
   Jordan Wong, Allan Fong, Nevin McVicar, Sally Smith, Joshua Giambattista, Derek Wells, Carter Kolbeck, Jonathan Giambattista, Lovedeep Gondara, Abraham Alexander · 2019 · Radiotherapy and Oncology
2. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights
   P. Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya Apte, Alexandre Ayotte, Bhakti Baheti, Spyridon Bakas, Andrea Bettinelli, Ronald Boellaard et al. · 2024 · Radiology
3. Weakly-supervised learning for lung carcinoma classification using deep learning
   Kanavati, Fahdi, Toyokawa, Gouji, Momosaki, Seiya, Rambeau, Michael, Kozuma, Yuka, Shoji, Fumihiro, Yamazaki, Koji, Takeo, Sadanori, Iizuka, Osamu, Tsuneki, Masayuki · 2020 · Scientific Reports
4. A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation
   Huiyan Jiang, Zhaoshuo Diao, Tianyu Shi, Yang Zhou, Feiyu Wang, Wenrui Hu, Xiaolin Zhu, Shijie Luo, Guoyu Tong, Yudong Yao · 2023 · Computers in Biology and Medicine
5. Emerging Applications of Artificial Intelligence in Neuro-Oncology
   Jeffrey D. Rudie, Andreas M. Rauschecker, Robert Nick Bryan, Christos A. Davatzikos, Suyash Mohan · 2019 · Radiology
6. Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017
   Jinzhong Yang, Harini Veeraraghavan, Samuel G. Armato, Keyvan Farahani, Justin Kirby, Jayashree Kalpathy–Cramer, Wouter van Elmpt, Andre L.A.J. Dekker, Xiao Han, Xue Feng et al. · 2018 · Medical Physics
7. Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
   Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch, Ruheena L. Mendes, Michelle Livne, Jeffrey De Fauw, Yojan Patel, Clemens Meyer, Harry Askham, Bernardino Romera‐Paredes et al. · 2018 · arXiv (Cornell University)
8. Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets
   Ravi K. Samala, Heang‐Ping Chan, Lubomir M. Hadjiiski, Mark A. Helvie, Caleb Richter, Kenny H. Cha · 2018 · IEEE Transactions on Medical Imaging
9. Criteria for the translation of radiomics into clinically useful tests
   Erich P. Huang, James P.B. O’Connor, Lisa M. McShane, Maryellen Lissak Giger, Philippe Lambin, Paul E. Kinahan, Eliot L. Siegel, Lalitha Krishna Shankar · 2022 · Nature Reviews Clinical Oncology
10. Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation
    Yinghuan Shi, Jian Zhang, Tong Ling, Jiwen Lu, Yefeng Zheng, Qian Yu, Lei Qi, Yang Gao · 2021 · IEEE Transactions on Medical Imaging
11. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness
    Yize Li, T. Mamie Lih, Saravana Mohan Dhanasekaran, Rahul Mannan, Lijun Chen, Marcin P. Cieslik, Yige Wu, Rita Jui-Hsien Lu, David J. Clark, Iga Kołodziejczak et al. · 2022 · Cancer Cell
12. NeRP: Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction
    Liyue Shen, John M. Pauly, Lei Xing · 2022 · IEEE Transactions on Neural Networks and Learning Systems
13. CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation
    Jian-Wei Lin, Jiatai Lin, Chenghao Lu, Hao Chen, Huan Lin, Bing-Chao Zhao, Zhen-Wei Shi, Bingjiang Qiu, Xipeng Pan, Ze-Yan Xu et al. · 2022 · IEEE Transactions on Medical Imaging
14. Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction
    Maged Nasser, Umi Kalsom Yusof · 2023 · Diagnostics
15. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis
    Wenqing Sun, Bin Zheng, Wei Qian · 2017 · Computers in Biology and Medicine
16. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Liu, Kao-Lang, Wu, Tinghui, Chen, Po-Ting, Tsai, Yuhsiang M, Roth, Holger, Wu, Ming-Shiang, Liao, Wei-Chih, Wang, Weichung · 2020 · The Lancet Digital Health
17. Overcoming Data Limitation in Medical Visual Question Answering
    Binh D. Nguyen, Thanh-Toan Do, Binh X. Nguyen, Tuong Do, Erman Tjiputra, Quang D. Tran · 2019 · Lecture notes in computer science
18. Understanding artificial intelligence based radiology studies: What is overfitting?
    Mutasa, Simukayi, Sun, Shawn, Ha, Richard · 2020 · Clinical Imaging
19. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
    Haonan Lu, Mubarik Arshad, Andrew Thornton, Giacomo Avesani, Paula Cunnea, Edward WJ Curry, Fahdi Kanavati, Jack Liang, Katherine Nixon, Sophie Therese Williams et al. · 2019 · Nature Communications
20. Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT
    Kensuke Umehara, Junko Ota, Takayuki Ishida · 2017 · Journal of Imaging Informatics in Medicine
21. Highly accurate model for prediction of lung nodule malignancy with CT scans
    Jason Causey, Junyu Zhang, Shiqian Ma, Bo Jiang, Jake Qualls, David G. Politte, Fred Prior, Shuzhong Zhang, Xiuzhen Huang · 2018 · Scientific Reports
22. Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
    Saed Khawaldeh, Usama Pervaiz, Azhar Rafiq, Rami S. Alkhawaldeh · 2017 · Applied Sciences
23. Machine Learning in oncology: A clinical appraisal
    Renato Cuocolo, Martina Caruso, Teresa Perillo, Lorenzo Ugga, Mario Petretta · 2020 · Cancer Letters
24. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure
    Fanny Orlhac, Augustin Lecler, Julien Savatovsky, Jessica Goya-Outi, Christophe Nioche, Frédérique Charbonneau, Nicholas Ayache, Frédérique Frouin, Loïc Duron, Irène Buvat · 2020 · European Radiology
25. Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation
    Jue Jiang, Yu‐Chi Hu, Neelam Tyagi, Pengpeng Zhang, Andreas Rimner, G. S. Mageras, Joseph O. Deasy, Harini Veeraraghavan · 2018 · Lecture notes in computer science
26. Deep learning in head & neck cancer outcome prediction
    André Diamant, Avishek Chatterjee, Martin Vallières, George Shenouda, Jan P. Seuntjens · 2019 · Scientific Reports
27. A Survey of Brain Tumor Segmentation and Classification Algorithms
    Erena Siyoum Biratu, Friedhelm Schwenker, Yehualashet Megersa Ayano, Taye Girma Debelee · 2021 · Journal of Imaging
28. Erratum to: Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
    Prateek Prasanna, Jay Patel, Sasan Partovi, Anant Madabhushi, Pallavi Tiwari · 2017 · European Radiology
29. Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT
    Yutong Xie, Jianpeng Zhang, Yong Xia · 2019 · Medical Image Analysis
30. A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom
    Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Juan David Velasquez · 2023 · Information Fusion
31. The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
    Spyridon Bakas, Chiharu Sako, Hamed Akbari, Michel Bilello, Aristeidis Sotiras, Gaurav Shukla, Jeffrey D. Rudie, Natali Alexandra Flores Santamaría, Anahita Fathi Kazerooni, Sarthak Pati et al. · 2022 · Scientific Data
32. A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma Segmentation and IDH Genotyping
    Jianhong Cheng, Jin Liu, Hulin Kuang, Jianxin Wang · 2022 · IEEE Transactions on Medical Imaging
33. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology
    Elena Fountzilas, Tillman E. Pearce, Mehmet A. Baysal, Abhijit Chakraborty, Apostolia Maria Tsimberidou · 2025 · npj Digital Medicine
34. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images
    Samuel G. Armato, HenkJan J. Huisman, Karen Drukker, Lubomir M. Hadjiiski, Justin Kirby, Nicholas Petrick, George Redmond, Maryellen Lissak Giger, Kenny Cha, Artem B. Mamonov et al. · 2018 · Journal of medical imaging
35. Classification of the glioma grading using radiomics analysis
    Hwan-ho Cho, Seung-Hak Lee, Jong‐Hoon Kim, Hyunjin Park · 2018 · PeerJ
36. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning
    Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost Willem Schouten, Rishi Nandoe Tewarie, Geert J. Lycklama à Nijeholt, Philip C. De Witt Hamer, Roelant S. Eijgelaar et al. · 2022 · Neuro-Oncology
37. Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
    Shengye Hu, Baiying Lei, Shuqiang Wang, Yong Wang, Zhiguang Feng, Yanyan Shen · 2021 · IEEE Transactions on Medical Imaging
38. The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset
    Evan Calabrese, Javier E. Villanueva-Meyer, Jeffrey D. Rudie, Andreas M. Rauschecker, Ujjwal Baid, Spyridon Bakas, Soonmee Cha, John Mongan, Christopher Paul Hess · 2022 · Radiology Artificial Intelligence
39. A hybrid deep CNN model for brain tumor image multi-classification
    Saravanan Srinivasan, Divya Francis, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Basu Dev Shivahare, Mohd Asif Shah · 2024 · BMC Medical Imaging
40. Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network
    Muhammad Rizwan, Aysha Shabbir, Abdul Rehman Javed, Maryam Shabbir, Thar Baker, Dhiya Al‐Jumeily · 2022 · IEEE Access
41. An introduction to deep learning in medical physics: advantages, potential, and challenges
    Chenyang Shen, Dan T. D. Nguyen, Zhiguo Zhou, Steve B Jiang, Bin Dong, Xun Jia · 2020 · Physics in Medicine and Biology
42. Agile convolutional neural network for pulmonary nodule classification using CT images
    Xinzhuo Zhao, Liyao Liu, Shouliang Qi, Yueyang Teng, Jianhua Li, Wei Qian · 2018 · International Journal of Computer Assisted Radiology and Surgery
43. WIRE: Wavelet Implicit Neural Representations
    Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
44. Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement
    Ken Chang, Andrew L. Beers, Harrison Xiao Bai, James M. Brown, Ina Ly, Xuejun Li, Joeky T. Senders, Vasileios K. Kavouridis, Alessandro Boaro, Chang Su et al. · 2019 · Neuro-Oncology
45. Clinically applicable deep learning framework for organs at risk delineation in CT images
    Hao Tang, Xuming Chen, Yang Liu, Zhipeng Lu, Junhua You, Mingzhou Yang, Shengyu Yao, Guoqi Zhao, Yi Xin Xu, Tingfeng Chen et al. · 2019 · Nature Machine Intelligence
46. Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation
    Mohammad Ashraf Ottom, Hanif Abdul Rahman, Ivo D. Dinov · 2022 · IEEE Journal of Translational Engineering in Health and Medicine
47. Automatic mass detection in mammograms using deep convolutional neural networks
    Richa Agarwal, Oliver Díaz, Xavier Lladó, Moi Hoon Yap, Robert Martí · 2019 · Journal of medical imaging
48. Brain Imaging Genomics: Integrated Analysis and Machine Learning
    Li Shen, Paul M. Thompson · 2019 · Proceedings of the IEEE
49. Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology
    Ferber, Dyke, El Nahhas, Omar S. M., Wölflein, Georg, Wiest, Isabella C., Clusmann, Jan, Leßmann, Marie-Elisabeth, Foersch, Sebastian, Lammert, Jacqueline, Tschochohei, Maximilian, Jäger, Dirk et al. · 2025 · Nature Cancer
50. A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond
    Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian, Shalini Subramanian, Aaron Carass, Jerry L. Prince, Yong Du · 2024 · Medical Image Analysis
51. How far have we come? Artificial intelligence for chest radiograph interpretation
    Kallianos, K., Mongan, J., Antani, S., Henry, T., Taylor, A., Abuya, J., Kohli, M. · 2019 · Clinical Radiology
52. Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma
    Moradmand, Hajar, Aghamiri, Seyed Mahmoud Reza, Ghaderi, Reza · 2020 · Journal of Applied Clinical Medical Physics
53. Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview
    Jun Alex Gao, Qian Jiang, Bo Zhou, Daozheng Chen · 2019 · Mathematical Biosciences & Engineering
54. Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions
    Luciano Monte Serrat Prevedello, Safwan S. Halabi, George Shih, Carol C. Wu, Marc D. Kohli, Falgun H. Chokshi, Bradley James Erickson, Jayashree Kalpathy–Cramer, Katherine P. Andriole, Adam Eugene Flanders · 2019 · Radiology Artificial Intelligence
55. Weakly supervised machine learning
    Zeyu Ren, Shuihua Wang‎, Yudong Zhang · 2023 · CAAI Transactions on Intelligence Technology
56. Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge
    Jun Ma, Yao Zhang, Song Gu, Xingle An, Zhihe Wang, Cheng Ge, Congcong Wang, Fan Zhang, Yu Wang, Yinan Xu et al. · 2022 · Medical Image Analysis
57. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma
    Zhu, Yitan, Li, Hui, Guo, Wentian, Drukker, Karen, Lan, Li, Giger, Maryellen L., Ji, Yuan · 2015 · Scientific Reports
58. Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules
    Xinyang Feng, Jie Yang, Andrew F. Laine, Elsa D. Angelini · 2017 · Lecture notes in computer science
59. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks
    Xia Huang, Wenqing Sun, Tzu-Liang Tseng, Chunqiang Li, Wei Qian · 2019 · Computerized Medical Imaging and Graphics
60. Using U-Net network for efficient brain tumor segmentation in MRI images
    Jason Walsh, Alice Othmani, Mayank Kumar Jain, Soumyabrata Dev · 2022 · Healthcare Analytics
61. Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases
    Shiv Ram Dubey, Satish Kumar Singh, Rajat Kumar Singh · 2015 · IEEE Transactions on Image Processing
62. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review
    Hafsa Bareen Syeda, Mahanazuddin Syed, Kevin Wayne Sexton, Shorabuddin Syed, Salma Begum, Farhanuddin Syed, Fred Prior, Feliciano Yu · 2020 · JMIR Medical Informatics
63. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy
    Xuming Chen, Shanlin Sun, Narisu Bai, Kun Han, Qianqian Liu, Shengyu Yao, Hao Tang, Chupeng Zhang, Zhipeng Lu, Qian Huang et al. · 2021 · Radiotherapy and Oncology
64. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms
    Yongye Su, Qian Liu, Wentao Xie, Pingzhao Hu · 2022 · Computer Methods and Programs in Biomedicine
65. Surgical spectral imaging
    Neil T. Clancy, Geoffrey Jones, Lena Maier‐Hein, Daniel S. Elson, Danail V. Stoyanov · 2020 · Medical Image Analysis
66. Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma
    Beig, Niha, Patel, Jay, Prasanna, Prateek, Hill, Virginia, Gupta, Amit, Correa, Ramon, Bera, Kaustav, Singh, Salendra, Partovi, Sasan, Varadan, Vinay et al. · 2018 · Scientific Reports
67. Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods
    Yuxiao Gao, Yang Jiang, Yanhong Peng, Fujiang Yuan, Xinyue Zhang, Jianfeng Wang · 2025 · Tomography
68. 2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer
    Chen Shen, Zhenyu Liu, Min Guan, Jiangdian Song, Yucheng Lian, Shuo Wang, Zhenchao Tang, Di Dong, Lingfei Kong, Meiyun Wang et al. · 2017 · Translational Oncology
69. A Customized VGG19 Network with Concatenation of Deep and Handcrafted Features for Brain Tumor Detection
    Venkatesan Rajinikanth, Alex Noel Joseph Raj, Palani Thanaraj Krishnan, Ganesh R. Naik · 2020 · Applied Sciences
70. Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database
    Yan, Ke, Wang, Xiaosong, Lu, Le, Zhang, Ling, Harrison, Adam P., Bagheri, Mohammadhadi, Summers, Ronald M. · 2018 · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
71. Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods
    Tomaž Vrtovec, Domen Močnik, Primož Strojan, Franjo Pernuš, Bulat Ibragimov · 2020 · Medical Physics
72. Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
    Oliver Díaz, Kaisar Kushibar, Richard O. Osuala, Akis Linardos, Lidia Garrucho, Laura Igual, Petia I. Radeva, Fred Prior, Polyxeni Gkontra, Karim Lekadir · 2021 · Physica Medica
73. Label-Free Segmentation of COVID-19 Lesions in Lung CT
    Qingsong Yao, Li Xiao, Peihang Liu, S. Kevin Zhou · 2021 · IEEE Transactions on Medical Imaging
74. Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer
    Nguyen Quoc Khanh Le, Quang Hien Kha, Nguyễn Văn Hiệp, Yung-Chieh Chen, Sho‐Jen Cheng, Cheng‐Yu Chen · 2021 · International Journal of Molecular Sciences
75. Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls
    Farhad Maleki, Katie L. Ovens, Rajiv Gupta, Caroline Reinhold, Alan Spatz, Reza Forghani · 2022 · Radiology Artificial Intelligence
76. Medical image super-resolution for smart healthcare applications: A comprehensive survey
    Sabina Umirzakova, Shabir Ahmad, Latif Ullah Khan, Taeg Keun Whangbo · 2023 · Information Fusion
77. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients
    Lifeng Yang, Jingbo Yang, Xiaobo Zhou, Liyu Huang, Weiling Zhao, Tao Wang, Jian Zhuang, Jie Tian · 2018 · European Radiology
78. Artificial intelligence for breast cancer analysis: Trends & directions
    Shahid Munir Shah, Rizwan Ahmed Khan, Sheeraz Arif, Unaiza Sajid · 2022 · Computers in Biology and Medicine
79. A review on brain tumor segmentation based on deep learning methods with federated learning techniques
    Md. Faysal Ahamed, Md. Munawar Hossain, Md. Nahiduzzaman, Md. Rabiul Islam, M. Robiul Islam, Mominul Ahsan, Julfikar Haider · 2023 · Computerized Medical Imaging and Graphics
80. Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network
    Nader Aldoj, Steffen Lukas, Marc Dewey, Tobias Penzkofer · 2019 · European Radiology
81. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
    Guo, Wentian, Li, Hui, Zhu, Yitan, Lan, Li, Yang, Shengjie, Drukker, Karen, Morris, Elizabeth A., Burnside, Elizabeth S., Whitman, Gary J., Giger, Maryellen L. et al. · 2015 · Journal of Medical Imaging
82. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms
    Isaac Shiri, Hasan Maleki, Ghasem Hajianfar, Hamid Abdollahi, Saeed Ashrafinia, Mathieu Hatt, Habib Zaidi, Mehrdad Oveisi, Arman Rahmim · 2020 · Molecular Imaging and Biology
83. Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma
    Benjamin Harris Kann, Daniel F. Hicks, Seyedmehdi Payabvash, Amit Kumar Mahajan, Justin Du, Vishal Gupta, Henry Soo-Min Park, James B. Yu, Wendell Gray Yarbrough, Barbara Ann Burtness et al. · 2019 · Journal of Clinical Oncology
84. The public cancer radiology imaging collections of The Cancer Imaging Archive
    Fred Prior, Kirk Smith, Ashish Sharma, Justin Kirby, Lawrence Tarbox, Ken Clark, William Bennett, Tracy S. Nolan, John Freymann · 2017 · Scientific Data
85. Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer
    Maryam Gholizadeh-Ansari, Javad Alirezaie, Paul Babyn · 2019 · Journal of Imaging Informatics in Medicine
86. Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images
    Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin · 2020 · Scientific Reports
87. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma
    Emily Rose Harding-Theobald, Jeremy Louissaint, Bharat Maraj, Edward Cuaresma, Whitney A. Townsend, Mishal Mendiratta‐Lala, Amit G. Singal, Grace L. Su, Anna Suk-Fong Lok, Neehar D. Parikh · 2021 · Alimentary Pharmacology & Therapeutics
88. Automated detection and segmentation of non-small cell lung cancer computed tomography images
    Sergey P. Primakov, Abdalla Ibrahim, Janita E. van Timmeren, Guangyao Wu, Simon Andreas Keek, Manon P. L. Beuque, Renée W. Y. Granzier, Elizaveta Lavrova, Madeleine Scrivener, Sebastian Sanduleanu et al. · 2022 · Nature Communications
89. Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma
    Niha G. Beig, Kaustav Bera, Prateek Prasanna, Jacob T. Antunes, Ramón Correa, Salendra Singh, Anas Saeed Bamashmos, Marwa Ismail, Nathaniel M. Braman, Ruchika Verma et al. · 2020 · Clinical Cancer Research
90. Prostate cancer classification from ultrasound and MRI images using deep learning based Explainable Artificial Intelligence
    Md. Rafiul Hassan, Md. Fakrul Islam, Md. Zia Uddin, Goutam Ghoshal, Mohammad Mehedi Hassan, Shamsul Huda, Giancarlo Fortino · 2021 · Future Generation Computer Systems
91. CT-ORG, a new dataset for multiple organ segmentation in computed tomography
    Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi W. Nobashi, Daniel L. Rubin · 2020 · Scientific Data
92. Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network
    Zhiwei Wang, Chaoyue Liu, Danpeng Cheng, Liang Wang, Xin Yan Yang, Kwang-Ting Tim Cheng · 2018 · IEEE Transactions on Medical Imaging
93. Images data practices for Semantic Segmentation of Breast Cancer using Deep Neural Network
    Luqman Ahmed, Muhammad Munwar Iqbal, Hamza Aldabbas, Shehzad Khalid, Yasir Saleem, Saqib Saeed · 2020 · Journal of Ambient Intelligence and Humanized Computing
94. Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models
    Asim Waqas, Marilyn M. Bui, Eric F. Glassy, Issam El Naqa, Piotr Borkowski, Andrew Borkowski, Ghulam Rasool · 2023 · Laboratory Investigation
95. OpenKBP: The open‐access knowledge‐based planning grand challenge and dataset
    Aaron Babier, Binghao Zhang, Rafid Mahmood, Kevin L. Moore, Thomas G. Purdie, Andrea L. McNiven, Timothy C. Y. Chan · 2021 · Medical Physics
96. Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
    Feng Shi, Weigang Hu, Jiaojiao Wu, Miaofei Han, Jiazhou Wang, Wei Zhang, Qing Zhou, Jingjie Zhou, Ying Wei, Ying Shao et al. · 2022 · Nature Communications
97. Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net
    Nader Aldoj, Federico Biavati, Florian Michallek, Sebastian Stober, Marc Dewey · 2020 · Scientific Reports
98. VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images
    Muhammad Attique Khan, Venkatesan Rajinikanth, Suresh Chandra Satapathy, David Taniar, Jnyana Ranjan Mohanty, Usman Tariq, Robertas Damaševičius · 2021 · Diagnostics
99. Improving breast cancer diagnostics with deep learning for MRI
    Jan Sylwester Witowski, Laura C. Heacock, Beatriu Reig, Stella K. Kang, Alana Amarosa Lewin, Kristine M. Pysarenko, Shalin J. Patel, Naziya Samreen, Wojciech Rudnicki, Elżbieta Łuczyńska et al. · 2022 · Science Translational Medicine
100. Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes
     Zaitsev, Aleksandr, Chelushkin, Maksim, Dyikanov, Daniiar, Cheremushkin, Ilya, Shpak, Boris, Nomie, Krystle, Zyrin, Vladimir, Nuzhdina, Ekaterina, Lozinsky, Yaroslav, Zotova, Anastasia et al. · 2022 · Cancer Cell