Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …

Graph convolutional neural networks have shown significant potential in natural and
histopathology images. However, their use has only been studied in a single magnification …

We propose a geometric deep-learning-based framework, TractGeoNet, for performing
regression using diffusion magnetic resonance imaging (dMRI) tractography and associated …

Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for
Whole Slide Image (WSI) analysis is challenging given the complexity of gigapixel slides …

Understanding the spatial heterogeneity of tumours and its links to disease initiation and
progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily …

This paper presents a novel approach to enhance the accuracy of patch-level Gleason
grading in prostate histopathology images, a critical task in the diagnosis and prognosis of …

Methods for unsupervised domain adaptation (UDA) help to improve the performance of
deep neural networks on unseen domains without any labeled data. Especially in medical …

Pathologists have, over several decades, developed criteria for diagnosing and grading
prostate cancer. However, this knowledge has not, so far, been included in the design of …

Abstract Background and Objective Pathology image classification is one of the most
essential auxiliary processes in cancer diagnosis. To overcome the problem of inadequate …