Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …

MemBrain v2 is a deep learning-enabled program aimed at the efficient analysis of
membranes in cryo-electron tomography (cryo-ET). The final v2 release of MemBrain will …

Recent progress in computational pathology has been driven by deep learning. While code
and data availability are essential to reproduce findings from preceding publications …

Stain variations often decrease the generalization ability of deep learning based
approaches in digital histopathology analysis. Two separate proposals, namely stain …

The color variation of hematoxylin and eosin (H&E)‐stained tissues has presented a
challenge for applications of artificial intelligence (AI) in digital pathology. Many color …

Convolutional neural networks (CNNs) have achieved remarkable segmentation accuracy
on benchmark datasets where training and test sets are from the same domain, yet their …

Deep learning has shown its effectiveness in histopathology image analysis, such as
pathology detection and classification. However, stain colour variation in Hematoxylin and …

Visual representation extraction is a fundamental problem in the field of computational
histopathology. Considering the powerful representation capacity of deep learning and the …

Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …