2 code implementations • 4 Jun 2018 • Sachin Mehta, Ezgi Mercan, Jamen Bartlett, Donald Weave, Joann G. Elmore, Linda Shapiro
In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis.
1 code implementation • 25 Jul 2020 • Sachin Mehta, Ximing Lu, Donald Weaver, Joann G. Elmore, Hannaneh Hajishirzi, Linda Shapiro
HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision.
1 code implementation • 11 Dec 2020 • Beibin Li, Ezgi Mercan, Sachin Mehta, Stevan Knezevich, Corey W. Arnold, Donald L. Weaver, Joann G. Elmore, Linda G. Shapiro
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification.
no code implementations • 16 Apr 2024 • Kechun Liu, Wenjun Wu, Joann G. Elmore, Linda G. Shapiro
Accurate cancer diagnosis remains a critical challenge in digital pathology, largely due to the gigapixel size and complex spatial relationships present in whole slide images.