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 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 2024:1 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