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

Cited by (1,012)

Items 701–800 of 1,012

1. MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models
   Numan Saeed, Muhammad Ridzuan, Hussain Alasmawi, Ikboljon Sobirov, Mohammad K. Yaqub · 2023 · Medical Image Analysis
2. Explicating the transformative role of artificial intelligence in designing targeted nanomedicine
   Masheera Akhtar, Nida Nehal, Azka Gull, Rabea Parveen, Sana Irfan Khan, Saba M. Khan, Javed Ali · 2025 · Expert Opinion on Drug Delivery
3. CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
   Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados · 2017 · Lecture notes in computer science
4. To share or not to share? Expected pros and cons of data sharing in radiological research
   Francesco Sardanelli, Marco Alì, M. G. Myriam Hunink, Nehmat Houssami, Luca Maria Sconfienza, Giovanni Di Leo · 2018 · European Radiology
5. Radiogenomics in renal cell carcinoma
   Francesco Alessandrino, Atul Bhanudas Shinagare, Dominick Bossé, Toni K. Choueiri, Katherine Maragaret Krajewski · 2018 · Abdominal Radiology
6. A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma
   Fan Lin, Changyi Ma, Jinpeng Xu, Lei Yi, Qing Li, Yong Lan, Ming Hua Sun, Wansheng Long, Enming Cui · 2020 · European Journal of Radiology
7. Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems
   Changhee Han, Leonardo Rundo, Kohei Murao, Takafumi Nemoto, Hideki Nakayama · 2020 · IFIP advances in information and communication technology
8. Reciprocal change in Glucose metabolism of Cancer and Immune Cells mediated by different Glucose Transporters predicts Immunotherapy response
   Kwon Joong Na, Hongyoon Choi, Ho Rim Oh, Yoon Ho Kim, Sae Bom Lee, Yoo Jin Jung, Jaemoon Koh, Samina Park, Hyun Joo Lee, Yoon Kyung Jeon et al. · 2020 · Theranostics
9. Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades
   Zaosong Zheng, Zhiliang Chen, Yingwei Xie, Qiyu Zhong, Wenlian Xie · 2021 · European Radiology
10. A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction
    Liyue Shen, Wei Zhao, Dante P. I. Capaldi, John M. Pauly, Lei Xing · 2022 · Computers in Biology and Medicine
11. Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network
    Mizuho Nishio, Koji Fujimoto, Hidetoshi Matsuo, Chisako Muramatsu, Ryo Sakamoto, Hiroshi Fujita · 2021 · Frontiers in Artificial Intelligence
12. A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images
    Kh Tohidul Islam, Sudanthi N. R. Wijewickrema, Stephen John O’Leary · 2022 · Sensors
13. Regional healthy brain activity, glioma occurrence and symptomatology
    Tianne Numan, Lucas C. Breedt, Bernardo de A.P.C. Maciel, Shanna D. Kulik, Jolanda Derks, Menno Michiel Schoonheim, Martin Klein, Philip C. De Witt Hamer, Julie J. Miller, Elizabeth Robins Gerstner et al. · 2022 · Brain
14. A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition
    Zahraa A. Al-Saffar, Tülay Yıldırım · 2020 · IEEE Access
15. Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations
    Martin A. Ebert, S. Gulliford, Oscar Acosta, R. de Crevoisier, Todd McNutt, Wilma D. Heemsbergen, Marnix G. Witte, Giuseppe Palma, T. Rancati, Claudio Fiorino · 2021 · Physics in Medicine and Biology
16. Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes
    Anthony Ortiz, Anusua Trivedi, Jocelyn Desbiens, Marian S. Blazes, Caleb Robinson, Sunil R. Gupta, Rahul M. Dodhia, Pavan K. Bhatraju, Wayne Conrad Liles, Aaron Lee et al. · 2022 · Scientific Reports
17. Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review
    Sonit Singh, Shakira Hoque, Amany Zekry, Arcot Sowmya · 2023 · Journal of Medical Systems
18. Lung cancer detection using digital image processing and artificial neural networks
    S. Kalaivani, Pramit Chatterjee, Shikhar Juyal, Rishi Gupta · 2017 · International Conference on Electronics, Communication and Aerospace Technology (ICECA)
19. Joint solution for PET image segmentation, denoising, and partial volume correction
    Ziyue Xu, Mingchen Gao, Georgios Z. Papadakis, Brian M Luna, Sanjay Kumar Jain, Daniel J. Mollura, Ulaş Bağcı · 2018 · Medical Image Analysis
20. A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network
    Zheng Ren Huang, Han Xu, Shun Su, Tianyu Wang, Yang Luo, Xingang Zhao, Yunhui Liu, Guoli Song, Yiwen Zhao · 2020 · Computers in Biology and Medicine
21. Comparative study of preclinical mouse models of high-grade glioma for nanomedicine research: the importance of reproducing blood-brain barrier heterogeneity
    Caterina Brighi, Lee B. Reid, Laura A. Genovesi, Marija Kojic, Amanda Millar, Zara C. Bruce, Alison L. White, Bryan W. Day, Stephen Rose, Andrew K. Whittaker et al. · 2020 · Theranostics
22. Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization
    Dipak Kumar Patra, Tapas Si, Sukumar Mondal, Prakash Mukherjee · 2021 · Biomedical Signal Processing and Control
23. A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data
    Beatriz Ocaña-Tienda, Julián Pérez-Beteta, José Villanueva-García, José Antonio Romero-Rosales, David Molina-García, Yannick Suter, Beatriz Asenjo, David Albillo, Ana Ortiz de Méndivil, Luís A. Pérez-Romasanta et al. · 2023 · Scientific Data
24. General purpose radiomics for multi-modal clinical research
    Michael Wels, Félix Lades, Alexander Mühlberg, Michael Suehling · 2019 · SPIE Medical Imaging: Computer-Aided Diagnosis
25. Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning
    Dufan Wu, Kyungsang Kim, Quanzheng Li · 2021 · Medical Physics
26. Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI
    Orcan Alpar, Rafael Doležal, Pavel Ryška, Ondřej Krejcar · 2022 · Pattern Recognition
27. Translation Consistent Semi-Supervised Segmentation for 3D Medical Images
    Y. Liu, Yu Tian, Chong Wang, Yuanhong Chen, Fengbei Liu, Vasileios Belagiannis, Gustavo Carneiro · 2024 · IEEE Transactions on Medical Imaging
28. From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients
    Yusheng Guo, Tianxiang Li, Bingxin Gong, Yan Hu, Sichen Wang, Lian Yang, Chuansheng Zheng · 2024 · Advanced Science
29. G-DOC Plus – an integrative bioinformatics platform for precision medicine
    Krithika Bhuvaneshwar, Anas Belouali, Varun Singh, Robert M. Johnson, Lei Song, Adil Alaoui, Michael A. Harris, Robert C. Clarke, Louis M. Weiner, Yuriy Gusev et al. · 2016 · BMC Bioinformatics
30. Content based medical image retrieval using topic and location model
    P. Shamna, V. K. Govindan, K. A. Abdul Nazeer · 2019 · Journal of Biomedical Informatics
31. Tumor Transcriptome Reveals High Expression of IL-8 in Non-Small Cell Lung Cancer Patients with Low Pectoralis Muscle Area and Reduced Survival
    Sarah Santiloni Cury, Diogo de Moraes, Paula Paccielli Freire, Grasieli de Oliveira, Douglas Venâncio Pereira Marques, Geysson Javier Fernández, Maeli Dal‐Pai‐Silva, Érica Nishida Hasimoto, Patrícia P. Reis, Sílvia Regina Rogatto et al. · 2019 · Cancers
32. Integrating Cross-modality Hallucinated MRI with CT to Aid Mediastinal Lung Tumor Segmentation
    Jiang Jue, Jason Hu, Neelam Tyagi, Andreas Rimner, Sean L. Berry, Joseph O. Deasy, Harini Veeraraghavan · 2019 · Lecture notes in computer science
33. A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography With Incomplete Data
    Jian Fu, Jianbing Dong, Feng Zhao · 2019 · IEEE Transactions on Image Processing
34. Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis
    Lucian Beer, Hilal Şahin, Nicholas W. Bateman, Ivana M. Blazic, Hebert Alberto Vargas, Harini Veeraraghavan, Justin Kirby, Brenda Fevrier-Sullivan, John Freymann, C. Carl Jaffe et al. · 2020 · European Radiology
35. Impact of Image Contrast Enhancement on Stability of Radiomics Feature Quantification on a 2D Mammogram Radiograph
    Siti Fairuz Mat Radzi, Muhammad Khalis Abdul Karim, M. Iqbal Saripan, Mohd Amiruddin Abd Rahman, Nurul Huda Osman, Entesar Zawam Dalah, Noramaliza Mohd Noor · 2020 · IEEE Access
36. Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma
    Niha G. Beig, Salendra Singh, Kaustav Bera, Prateek Prasanna, Gagandeep Singh, Jonathan Chen, Anas Saeed Bamashmos, Addison E. Barnett, Kyle Hunter, Volodymyr Statsevych et al. · 2020 · Neuro-Oncology
37. Style transfer strategy for developing a generalizable deep learning application in digital pathology
    Seo Jeong Shin, Seng Chan You, Hokyun Jeon, Ji Won Jung, Min Ho An, Rae Woong Park, Jin Roh · 2020 · Computer Methods and Programs in Biomedicine
38. Lung Cancer Radiomics: Highlights from the IEEE Video and Image Processing Cup 2018 Student Competition [SP Competitions]
    Arash Mohammadi, Parnian Afshar, Amir A. Asif, Keyvan Farahani, Justin Kirby, Anastasia Oikonomou, Konstantinos N. Plataniotis · 2018 · IEEE Signal Processing Magazine
39. Bridging gaps between images and data: a systematic update on imaging biobanks
    Michela Gabelloni, Lorenzo Faggioni, Rita Borgheresi, Giuliana Restante, Jorge Eduardo Shortrede, Lorenzo Tumminello, Camilla Scapicchio, Francesca Coppola, Dania Cioni, Ignacio Gómez-Rico Junquero et al. · 2022 · European Radiology
40. Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models
    Matteo Ferrante, Lisa M. Rinaldi, Francesca Botta, Xiaobin Hu, Andreas Dolp, Marta Minotti, Francesca De Piano, Gianluigi Funicelli, Stefania Volpe, Federica Bellerba et al. · 2022 · Journal of Clinical Medicine
41. Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing
    Abhilash Pati, Manoranjan Parhi, Binod Kumar Pattanayak, Debabrata Singh, Vijendra Pratap Singh, Seifedine Kadry, Yunyoung Nam, Byeong-Gwon Kang · 2023 · Diagnostics
42. Classification of non-small cell lung cancer types using sparse deep neural network features
    Anil Kumar Swain, Aleena Swetapadma, Jitendra Kumar Rout, Bunil Kumar Balabantaray · 2023 · Biomedical Signal Processing and Control
43. A statistical deformation model-based data augmentation method for volumetric medical image segmentation
    Wenfeng He, Chulong Zhang, Jingjing Dai, Lin Liu, Tangsheng Wang, Xuan Liu, Yuming Jiang, Na Li, Jing Xiong, Lei Wang et al. · 2023 · Medical Image Analysis
44. Evaluating ChatGPT-4V in chest CT diagnostics: a critical image interpretation assessment
    Reza Dehdab, Andreas Brendlin, Sebastian Werner, Haidara Almansour, Sebastian Gassenmaier, Jan Michael Brendel, Konstantin Nikolaou, Saif Afat · 2024 · Japanese Journal of Radiology
45. uniGradICON: A Foundation Model for Medical Image Registration
    Lin Tian, Hastings Greer, Roland Kwitt, François‐Xavier Vialard, Raúl San Jośe Estépar, Sylvain Bouix A, Jarrett Rushmore, Marc Niethammer · 2024 · Lecture notes in computer science
46. Automated Segmentation of Abdominal Skeletal Muscle on Pediatric CT Scans Using Deep Learning
    James Castiglione, Elanchezhian Somasundaram, Leah A. Gilligan, Andrew T. Trout, Samuel L. Brady · 2021 · Radiology Artificial Intelligence
47. Standardization of imaging methods for machine learning in neuro-oncology
    Xiao Tian Li, Raymond Y. Huang · 2020 · Neuro-Oncology Advances
48. Lung cancer diagnosis using deep attention‐based multiple instance learning and radiomics
    Junhua Chen, Haiyan Zeng, Chong Zhang, Zhenwei Shi, Andre L.A.J. Dekker, Leonard Wee, Iñigo Bermejo · 2022 · Medical Physics
49. Artificial intelligence in radiotherapy: a technological review
    Ke Sheng · 2020 · Frontiers of Medicine
50. Tumor heterogeneity estimation for radiomics in cancer
    Ani Eloyan, Mun Sang Yue, Davit Khachatryan · 2020 · Statistics in Medicine
51. Evaluation of brain tumor using brain MRI with modified-moth-flame algorithm and Kapur’s thresholding: a study
    Seifedine Kadry, Venkatesan Rajinikanth, Nadaradjane Sri Madhava Raja, D. Jude Hemanth, Naeem M. S. Hannoon, Alex Noel Joseph Raj · 2021 · Evolutionary Intelligence
52. The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
    Abdalla Ibrahim, Turkey Refaee, Ralph T. H. Leijenaar, Sergey P. Primakov, Roland Hustinx, Felix Manuel Mottaghy, Henry C. Woodruff, Andrew D. A. Maidment, Philippe Lambin · 2021 · PLoS ONE
53. Improving Generalization of Deep Learning Models for Diagnostic Pathology by Increasing Variability in Training Data: Experiments on Osteosarcoma Subtypes
    Haiming Tang, Nanfei Sun, Steven S. Shen · 2021 · Journal of Pathology Informatics
54. Pediatric chest‐abdomen‐pelvis and abdomen‐pelvis CT images with expert organ contours
    Petr Jordan, Philip M. Adamson, Vrunda Bhattbhatt, Surabhi Beriwal, Sangyu Shen, Oskar Radermecker, Supratik Bose, Linda S. Strain, Michael Offe, David Fraley et al. · 2022 · Medical Physics
55. iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images
    Pedro C. Neto, Sara P. Oliveira, Diana Montezuma, João Baptista de Paula Fraga, Ana Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel Pinto, Jaime S. Cardoso · 2022 · Cancers
56. Deep learning integrates histopathology and proteogenomics at a pan-cancer level
    Joshua M. Wang, Runyu Hong, Elizabeth G. Demicco, Jimin Tan, Rossana Lazcano, Andre Luis Moreira, Yize Li, Anna Pamela Calinawan, Narges Razavian, Tobias Schraink et al. · 2023 · Cell Reports Medicine
57. Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics
    Ghasem Hajianfar, Atlas Haddadi Avval, Seyyed Ali Hosseini, Mostafa Nazari, Mehrdad Oveisi, Isaac Shiri, Habib Zaidi · 2023 · La radiologia medica
58. Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets
    A. Tripathi, A. Waqas, Kavya Venkatesan, Yasin Yilmaz, Ghulam Rasool · 2023 · Italian National Conference on Sensors
59. A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification
    Efe Precious Onakpojeruo, Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin, İlker Özşahin · 2024 · Brain Sciences
60. Knowledge distillation and teacher–student learning in medical imaging: Comprehensive overview, pivotal role, and future directions
    Xiang Li, Like Li, Minglei Li, Pengfei Yan, Ting Feng, Hao Luo, Yong Le Zhao, Yin Shen · 2025 · Medical Image Analysis
61. The path from big data to precision medicine
    Bevan Emma Huang, Widya Mulyasasmita, Gunaretnam Rajagopal · 2016 · Expert Review of Precision Medicine and Drug Development
62. Methylation of L1RE1, RARB, and RASSF1 function as possible biomarkers for the differential diagnosis of lung cancer
    Robert F. H. Walter, P. Rozynek, Swaantje Casjens, R. Werner, Fabian Dominik Mairinger, Ernst‐Jan M. Speel, Axel zur Hausen, S. Meier, Jeremias Wohlschlaeger, Dirk Theegarten et al. · 2018 · PLoS ONE
63. Classification of suspicious lesions on prostate multiparametric MRI using machine learning
    Deukwoo Kwon, Isildinha Marques dos Reis, Adrian Breto, Yohann Tschudi, Nicole Gautney, Olmo Zavala‐Romero, Christopher Lopez, John C. Ford, Sanoj Punnen, Alan S Pollack et al. · 2018 · Journal of medical imaging
64. Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors
    Chang Gong, Robert A. Anders, Qingfeng Zhu, Janis Marie Taube, Benjamin Green, Wenting Cheng, Imke Heleen Bartelink, Paolo Vicini, Bing Wang, Aleksander S. Popel · 2019 · Frontiers in Oncology
65. Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network
    Jeffrey D. Rudie, David A Weiss, Rachit Saluja, Andreas M. Rauschecker, Jiancong Wang, Leo P. Sugrue, Spyridon Bakas, John B. Colby · 2019 · Frontiers in Computational Neuroscience
66. Multiscale receptive field based on residual network for pancreas segmentation in CT images
    Feiyan Li, Weisheng Li, Yucheng Shu, Shengfeng Qin, Bin Xiao, Ziwei Zhan · 2019 · Biomedical Signal Processing and Control
67. Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database
    Jeremy T. Moreau, Todd Cameron Hankinson, Sylvain Baillet, Roy Dudley · 2020 · npj Digital Medicine
68. Automatic Pancreas Segmentation Using Coarse-Scaled 2D Model of Deep Learning: Usefulness of Data Augmentation and Deep U-Net
    Mizuho Nishio, Shunjiro Noguchi, Koji Fujimoto · 2020 · Applied Sciences
69. Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma
    Luyuan Zhang, Felipe Giuste, Juan C. Vizcarra, Xuejun Li, David A. Gutman · 2020 · Frontiers in Oncology
70. PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines
    Kendall J. Kiser, Sara Ahmed, Sonja Stieb, Abdallah Mohamed, Hesham M. Elhalawani, Peter Y. S. Park, Nathan S. Doyle, Brandon J. Wang, Arko Barman, Li Zhao et al. · 2020 · Medical Physics
71. Two‐stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra‐reader and inter‐reader reliability at full and reduced radiation dose on an external dataset
    Panda, Ananya, Korfiatis, Panagiotis, Suman, Garima, Garg, Sushil K., Polley, Eric C., Singh, Dhruv P., Chari, Suresh T., Goenka, Ajit H. · 2021 · Medical Physics
72. Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer
    Joana Morgado, Tânia Pereira, Francisco Silva, Cláudia Freitas, Eduardo Negrão, Beatriz Flor de Lima, Miguel Correia da Silva, António J. Madureira, Isabel M. Ramos, Venceslau Pinto Hespanhol et al. · 2021 · Applied Sciences
73. Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms
    Juhun Lee, Robert M. Nishikawa · 2021 · IEEE Transactions on Medical Imaging
74. Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis
    Yiling Wang, Elia Lombardo, Michele Avanzo, Sebastian Zschaeck, Julian Weingärtner, Adrien Holzgreve, Nathalie L. Albert, Sebastian Norbert Marschner, Giuseppe Fanetti, Giovanni Franchin et al. · 2022 · Computer Methods and Programs in Biomedicine
75. Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study
    Sulaiman Vesal, Iani J. M. B. Gayo, Indrani Bhattacharya, Shyam Natarajan, Leonard S. Marks, Dean C. Barratt, Richard E. Fan, Yipeng Hu, Geoffrey A. Sonn, Mirabela Rusu · 2022 · Medical Image Analysis
76. A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future
    Joe Logan, Paul J Kennedy, Daniel Robin Catchpoole · 2023 · Scientific Data
77. Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs
    Takafumi Nemoto, Natsumi Futakami, Etsuo Kunieda, Masamichi Yagi, Atsuya Takeda, Takeshi Akiba, Eride Mutu, Naoyuki Shigematsu · 2021 · Radiological Physics and Technology
78. Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomic, and Demographic Data
    Can Cui, Han Liu, Quan Liu, Ruining Deng, Zuhayr Asad, Yaohong Wang, Shilin Zhao, Haichun Yang, Bennett A. Landman, Yuankai Huo · 2022 · Lecture notes in computer science
79. Transcriptomic and connectomic correlates of differential spatial patterning among gliomas
    Rafael Romero-García, Ayan S. Mandal, Richard A. I. Bethlehem, Benedicto Crespo‐Facorro, Michael G. Hart, John Suckling · 2022 · Brain
80. Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review
    Junhua Chen, Shenlun Chen, Leonard Wee, Andre L.A.J. Dekker, Iñigo Bermejo · 2023 · Physics in Medicine and Biology
81. B 2 C 3 NetF 2 : Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection
    Mamuna Fatima, Muhammad Attique Khan, Saima Shaheen, Nouf Abdullah Almujally, Shuihua Wang‎ · 2023 · CAAI Transactions on Intelligence Technology
82. An efficient Intra-Inter pixel encryption scheme to secure healthcare images for an IoT environment
    Sachikanta Dash, Sasmita Kumari Padhy, S Anjali Devi, Savya Sachi, K. Abhimanyu Kumar Patro · 2023 · Expert Systems with Applications
83. Complementary consistency semi-supervised learning for 3D left atrial image segmentation
    Hejun Huang, Zuguo Chen, Chaoyang Chen, Ming Lu, Ying Yong Zou · 2023 · Computers in Biology and Medicine
84. A systematic review of machine and deep learning techniques for the identification and classification of breast cancer through medical image modalities
    Neha Thakur, Pardeep Kumar, A. P. Siva Kumar · 2023 · Multimedia Tools and Applications
85. SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training Model for Semi-Supervised Medical Image Segmentation
    Juzheng Miao, Si-Ping Zhou, Guangquan Zhou, Kai-Ni Wang, Meng Yang, Shou-Jun Zhou, Yang Chen · 2023 · IEEE Transactions on Medical Imaging
86. ChatGPT for digital pathology research
    Mohamed Omar, Varun Ullanat, Massimo F. Loda, Luigi Marchionni, Renato Umeton · 2024 · The Lancet Digital Health
87. Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis
    Oladosu Oyebisi Oladimeji, Hamail Ayaz, Ian Vince McLoughlin, Saritha Unnikrishnan · 2024 · Results in Engineering
88. An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images
    Xinqi Wang, Keming Mao, Lizhe Wang, Peiyi Yang, Duo Lu, Ping He · 2019 · Sensors
89. Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis
    Takahiro Nakamoto, Wataru Takahashi, Akihiro Haga, Satoshi Takahashi, Shigeru Kiryu, Kanabu Nawa, T. Ohta, Sho Ozaki, Yuki Nozawa, Shota Tanaka et al. · 2019 · Scientific Reports
90. Classification of magnetic resonance images for brain tumour detection
    Yashwant Kurmi, Vijayshri Chaurasia · 2020 · IET Image Processing
91. Automating proton treatment planning with beam angle selection using Bayesian optimization
    Vicki Trier Taasti, Linda X. Hong, Jin Sup Shim, Joseph O. Deasy, Masoud Zarepisheh · 2020 · Medical Physics
92. Automated Quality Assurance of OAR Contouring for Lung Cancer Based on Segmentation With Deep Active Learning
    Kuo Men, Huaizhi Geng, Tithi Biswas, Zhongxing X. Liao, Ying Xiao · 2020 · Frontiers in Oncology
93. Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator
    Jianning Chi, Chengdong Wu, Xiaosheng Yu, Peng Ji, Hao Chu · 2020 · IEEE Access
94. Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques
    Ioannis D. Apostolopoulos, Emmanuel G. Pintelas, Ioannis E. Livieris, Dimitris J. Apostolopoulos, Nikolaos D. Papathanasiou, Panagiotis Pintelas, George Panayiotakis · 2021 · Medical & Biological Engineering & Computing
95. Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function
    Zhihua Li, Weili Shi, Qiwei Xing, Yu Miao, Wei He, Huamin Yang, Zhengang Jiang · 2021 · Computational and Mathematical Methods in Medicine
96. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
    Hwan-ho Cho, Ho Yun Lee, Eunjin Kim, Geewon Lee, Jong‐Hoon Kim, Junmo Kwon, Hyunjin Park · 2021 · Communications Biology
97. CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools
    Luis Martí‐Bonmatí, Ana Miguel, A. Suárez‐García, Mario Aznar, Jean-Paul Bérégi, Laure S Fournier, Emanuele Neri, Andrea Laghi, Manuela França, Francesco Sardanelli et al. · 2022 · Frontiers in Oncology
98. Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm
    Gopal Singh Tandel, Ashish Tiwari, Omprakash G. Kakde · 2022 · Biomedical Signal Processing and Control
99. A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI
    Shahriar Faghani, Bardia Khosravi, Mana Moassefi, Gian Marco Conte, Bradley James Erickson · 2023 · Journal of Imaging Informatics in Medicine
100. ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
     Zihan Li, Yuan Zheng, Xiangde Luo, Dandan Shan, Qingqi Hong · 2023 · ACM International Conference on Multimedia (ACM MM)