8 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
Centroid-aware feature recalibration for cancer grading in pathology images
Jaeung Lee, Keunho Byeon, Jin Tae Kwak
Cancer grading is an essential task in pathology. The recent developments of artificial neural networks in computational pathology have shown that these methods hold great potentia…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
Trinh Thi Le Vuong, Quoc Dang Vu, Mostafa Jahanifar +3
The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift…
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza +4
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of au…
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Quoc Dang Vu, Simon Graham, Minh Nguyen Nhat To +11
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help…