activity
20172021
most citedRepresentation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images

18 citations · 22 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CV20211 cited

Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson +8

The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. Howe…

eess.IV20213 cited

Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

Richard J. Chen, Ming Y. Lu, Muhammad Shaban +4

Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Desp…

eess.IV2021

A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

Muhammad Shaban, Shan E Ahmed Raza, Mariam Hassan +11

The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim…

eess.IV2019

CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

Yanning Zhou, Simon Graham, Navid Alemi Koohbanani +3

Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tis…

eess.IV2019

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…

cs.CV2018

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…