Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36427, first published .
Uncertainty Estimation in Medical Image Classification: Systematic Review

Uncertainty Estimation in Medical Image Classification: Systematic Review

Uncertainty Estimation in Medical Image Classification: Systematic Review

Journals

  1. Martin R, Duong L. Pixel-wise confidence estimation for segmentation in Bayesian Convolutional Neural Networks. Machine Vision and Applications 2023;34(1) View
  2. Maron R, Hekler A, Haggenmüller S, von Kalle C, Utikal J, Müller V, Gaiser M, Meier F, Hobelsberger S, Gellrich F, Sergon M, Hauschild A, French L, Heinzerling L, Schlager J, Ghoreschi K, Schlaak M, Hilke F, Poch G, Korsing S, Berking C, Heppt M, Erdmann M, Haferkamp S, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather J, Fröhling S, Lipka D, Krieghoff-Henning E, Brinker T. Model soups improve performance of dermoscopic skin cancer classifiers. European Journal of Cancer 2022;173:307 View
  3. Petersen E, Holm S, Ganz M, Feragen A. The path toward equal performance in medical machine learning. Patterns 2023;4(7):100790 View
  4. Alves N, Bosma J, Venkadesh K, Jacobs C, Saghir Z, de Rooij M, Hermans J, Huisman H. Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT. Radiology 2023;308(3) View
  5. Lambert B, Forbes F, Doyle S, Dehaene H, Dojat M. Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis. Artificial Intelligence in Medicine 2024;150:102830 View
  6. Nakagawa S, Ono N, Hakamata Y, Ishii T, Saito A, Yanagimoto S, Kanaya S, McGinnis R. Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning. PLOS Digital Health 2024;3(3):e0000460 View
  7. Peeters D, Alves N, Venkadesh K, Dinnessen R, Saghir Z, Scholten E, Schaefer-Prokop C, Vliegenthart R, Prokop M, Jacobs C. Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation. European Radiology 2024;34(10):6639 View
  8. Hussain D, Al-masni M, Aslam M, Sadeghi-Niaraki A, Hussain J, Gu Y, Naqvi R. Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations. Journal of X-Ray Science and Technology 2024;32(4):857 View
  9. Mushtaq F, Bhattacharjee S, Mandia S, Singh K, Chouhan S, Kumar R, Harjule P. Artificial intelligence for computer aided detection of pneumoconiosis: A succinct review since 1974. Engineering Applications of Artificial Intelligence 2024;133:108516 View
  10. Joskowicz L, Di Veroli B, Lederman R, Shoshan Y, Sosna J. Three scans are better than two for follow-up: An automatic method for finding missed and misidentified lesions in cross-sectional follow-up of oncology patients. European Journal of Radiology 2024;176:111530 View
  11. Du X. Uncertainty Separation Method for Simulation With Image and Numerical Data. Journal of Verification, Validation and Uncertainty Quantification 2024;9(1) View
  12. Stember J, Dishner K, Jenabi M, Pasquini L, K Peck K, Saha A, Shah A, O’Malley B, Ilica A, Kelly L, Arevalo-Perez J, Hatzoglou V, Holodny A, Shalu H. Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization. Journal of Imaging Informatics in Medicine 2024 View
  13. Zandehshahvar M, van Assen M, Kim E, Kiarashi Y, Keerthipati V, Tessarin G, Muscogiuri E, Stillman A, Filev P, Davarpanah A, Berkowitz E, Tigges S, Lee S, Vey B, De Cecco C, Adibi A. Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset. Journal of Imaging Informatics in Medicine 2024 View
  14. Borah J, Singh H, Sarmah K. A Deep Ensemble Approach for Lung Disease Classification in Chest X-Ray Across Data Distribution Shifts and Unseen Data Generalization. SN Computer Science 2024;5(7) View
  15. Du L, Gao P, Liu Z, Yin N, Wang X. TMODINET: A trustworthy multi-omics dynamic learning integration network for cancer diagnostic. Computational Biology and Chemistry 2024;113:108202 View
  16. Wahid K, Kaffey Z, Farris D, Humbert-Vidan L, Moreno A, Rasmussen M, Ren J, Naser M, Netherton T, Korreman S, Balakrishnan G, Fuller C, Fuentes D, Dohopolski M. Artificial intelligence uncertainty quantification in radiotherapy applications − A scoping review. Radiotherapy and Oncology 2024;201:110542 View
  17. Chen C, Zhao L, Lang Q, Xu Y. A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language. Bioengineering 2024;11(10):993 View
  18. Wahed M, Alqaraleh M, Alzboon M, Subhi Al-Batah M. AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics. Seminars in Medical Writing and Education 2025;4:35 View
  19. Islam S, Deo R, Datta Barua P, Soar J, Yu P, Rajendra Acharya U. Retinal Health Screening Using Artificial Intelligence With Digital Fundus Images: A Review of the Last Decade (2012–2023). IEEE Access 2024;12:176630 View
  20. Dadjouy S, Sajedi H. Gallbladder cancer detection via ultrasound image analysis: An end‐to‐end hierarchical feature‐fused model. IET Image Processing 2024 View

Books/Policy Documents

  1. Taguelmimt K, Dang H, Miranda G, Visvikis D, Malavaud B, Bert J. Cancer Prevention, Detection, and Intervention. View