2 citations · 6 across the 5 of their papers we have counts for
6 papers
Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency
Sitong Liu, Kechun Liu, Samuel Margolis +6
Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining fr…
Semantics-Aware Attention Guidance for Diagnosing Whole Slide Images
Kechun Liu, Wenjun Wu, Joann G. Elmore +1
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. Trad…
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline
Beibin Li, Ezgi Mercan, Sachin Mehta +5
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…
HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
Sachin Mehta, Ximing Lu, Donald Weaver +3
Training end-to-end networks for classifying gigapixel size histopathological images is computationally intractable. Most approaches are patch-based and first learn local represent…
Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
Sachin Mehta, Ezgi Mercan, Jamen Bartlett +3
In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis. Our method effic…
Learning to Segment Breast Biopsy Whole Slide Images
Sachin Mehta, Ezgi Mercan, Jamen Bartlett +3
We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoder-decoder networks…