MuTILs: explainable, multiresolution computational scoring of Tumor-Infiltrating Lymphocytes in breast carcinomas using clinical guidelines
Keyword(s):
Tumor-Infiltrating Lymphocytes (TILs) have strong prognostic and predictive value in breast cancer, but their visual assessment is subjective. We present MuTILs, a convolutional neural network architecture specifically optimized for the assessment of TILs in whole-slide image scans in accordance with clinical scoring recommendations. MuTILs is a concept bottleneck model, designed to be explainable and to encourage sensible predictions at multiple resolutions. Our computational scores match visual scores and have independent prognostic value in invasive breast cancers from the TCGA dataset.
2016 ◽
Vol 158
(1)
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pp. 1-9
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2018 ◽
Vol 17
(6)
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pp. 1324-1331
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2017 ◽
Vol 168
(1)
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pp. 135-145
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