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

Numerous machine learning (ML) models have been developed for breast cancer using
various types of data. Successful external validation (EV) of ML models is important …

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

Task-specific deep learning models in histopathology offer promising opportunities for
improving diagnosis, clinical research, and precision medicine. However, development of …

The introduction of machine learning in digital pathology has deeply impacted the field,
especially with the advent of whole slide image (WSI) analysis. In this review, we tried to …

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 …

Self-supervised learning (SSL) has emerged as a key technique for training networks that
can generalize well to diverse tasks without task-specific supervision. This property makes …

Ovarian carcinoma is known for its diverse subtypes with unique morphologies and clinical
characteristics, causing considerable diagnostic complexities. While deep learning has …

Over the last ten years, Artificial Intelligence (AI) has significantly contributed to solving
several health issues, such as cancer. Deep Learning (DL), a subset of adaptable AI that …

This paper presents a new approach for classifying 2D histopathology patches using few-
shot learning. The method is designed to tackle a significant challenge in histopathology …