Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue.

Background: Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model's viability and applicability in the diagnostic process.

Attention-Enhanced Unpaired xAI-GANs for Transformation of Histological Stain Images.

Histological staining is the primary method for confirming cancer diagnoses, but certain types, such as p63 staining, can be expensive and potentially damaging to tissues. In our research, we innovate by generating p63-stained images from H&E-stained slides for metaplastic breast cancer. This is a crucial development, considering the high costs and tissue risks associated with direct p63 staining.

Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms.

In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes.

MLtox, online phototoxicity prediction webpage.

Phototoxicity, sometimes in the literature referred to as photo-irritation, is a chemically induced reaction requiring light. While it is generally accepted that phototoxicity testing can be performed in the majority of cases in vitro (i.e.