most citedWhy is the winner the best?

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2023

An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer

Adam J Shephard, Mostafa Jahanifar, Ruoyu Wang +6

Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast c…

cs.CV20231 cited

Why is the winner the best?

Matthias Eisenmann, Annika Reinke, Vivienn Weru +122

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…

cs.CV20231 cited

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…

eess.IV20231 cited

Nuclear Segmentation and Classification: On Color & Compression Generalization

Quoc Dang Vu, Robert Jewsbury, Simon Graham +5

Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize we…

cs.CV2022

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…