Clinical grade computional pathology using weakly supervised deep learning on whole slide images
Abstract 需要大量手工標註數據集一直阻礙病理學方面的決策支持系統的發展以及在臨牀上部署。爲了解決這一問題，本文提出了基於多實例學習的深度學習系統，其僅僅使用已報告的診斷作爲訓練的標籤，得意邊廣泛且費時間的逐像素手工標註。本文在來自15187位病人的44732張全切片圖像構成的數據集上評估了該框架的性能，並且這些數據沒有經過任何整理(curation)。前列腺癌、基底細胞癌和乳腺癌轉移到腋窩
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相關文章
- 1. Thinking in Weakly Supervised Learning
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