1 citations · 2 across the 3 of their papers we have counts for
6 papers
Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations
Noorul Wahab, Islam M Miligy, Katherine Dodd +24
Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, an…
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
Jevgenij Gamper, Brandon Chan, Yee Wah Tsang +2
To train a robust deep learning model, one usually needs a balanced set of categories in the training data. The data acquired in a medical domain, however, frequently contains an a…
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Muhammad Shaban, Ruqayya Awan, Muhammad Moazam Fraz +3
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally…
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…
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features
Talha Qaiser, Yee-Wah Tsang, Daiki Taniyama +4
Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based…
Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer
Korsuk Sirinukunwattana, David Snead, David Epstein +5
Distant metastasis is the major cause of death in colorectal cancer (CRC). Patients at high risk of developing distant metastasis could benefit from appropriate adjuvant and follow…