Prostate cancer grade using self-supervised learning and novel feature aggregator based on weakly-labeled gbit-pixel pathology images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-05224-w.pdf
Reference40 articles.
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3. Bulten W, Pinckaers H, Boven H, Vink R, Bel T, Ginneken B, Laak J, Kaa CH-v, Litjens G (2020) Automated deep-learning system for gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol 21:233–241. https://doi.org/10.1016/S1470-2045(19)30739-9
4. Tellez D, Litjens G, Laak J, Ciompi F (2021) Neural image compression for gigapixel histopathology image analysis. IEEE Transactions on pattern analysis and machine intelligence 43:567–578. https://doi.org/10.1109/TPAMI.2019.2936841
5. Wang J, Mao X, Wang Y, Tao X, Chu J, Li Q (2023) Automatic generation of pathological benchmark dataset from hyperspectral images of double stained tissues. OpticsOptics & Laser Technology 163:109331. https://doi.org/10.1016/j.optlastec.2023.109331
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