This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology
images is a fundamental prerequisite in the digital pathology work-flow. The development of …

In pathological diagnostics, histological images highlight the oncological features of excised
specimens, but they require laborious and costly staining procedures. Despite recent …

The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …

Although having achieved great success in medical image segmentation, deep
convolutional neural networks usually require a large dataset with manual annotations for …

Detecting various types of cells in and around the tumor matrix holds a special significance
in characterizing the tumor micro-environment for cancer prognostication and research …

In this work we present an experimental setup to semi automatically obtain exhaustive nuclei
labels across 19 different tissue types, and therefore construct a large pan-cancer dataset for …

The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is
an important prerequisite in designing a computer-aided diagnostics (CAD) system for …

Recently developed approaches for highly multiplexed imaging have revealed complex
patterns of cellular positioning and cell-cell interactions with important roles in both cellular …