In modern clinical practice, digital pathology has a crucial role and is increasingly a
technological requirement in the scientific laboratory environment. The advent of whole-slide …

Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …

Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …

With the increasing ability to routinely and rapidly digitize whole slide images with slide
scanners, there has been interest in develo** computerized image analysis algorithms for …

Biomedical applications of deep learning algorithms rely on large expert annotated data
sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone …

Several reports have shown that radiomic features are affected by acquisition and
reconstruction parameters, thus hampering multicenter studies. We propose a method that …

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of
digital pathology (DP) and an increase in computational power have led to the development …

Purpose The aim of this study was to validate previously developed radiomics models
relying on just two radiomics features from 18 F-fluorodeoxyglucose positron emission …

Medical activity requires responsibility not only based on knowledge and clinical skills, but
also in managing a vast amount of information related to patient care. It is through the …

Accurate interpretation of the hematoxylin and eosin (H&E) slide has remained the
foundation of pathological analysis and diagnostic medicine for over a century. 1 For the …