Background: Deep learning (DL) is a representation learning approach ideally suited for
image analysis challenges in digital pathology (DP). The variety of image analysis tasks in …

Clinical efforts on precision medicine are driving the need for accurate diagnostic, new
prognostic and novel drug predictive assays to inform patient selection and stratification for …

The field of immuno-oncology has expanded rapidly over the past decade, but key questions
remain. How does tumour-immune interaction regulate disease progression? How can we …

Determining the grade of colon cancer from tissue slides is a routine part of the pathological
analysis. In the case of colorectal adenocarcinoma (CRA), grading is partly determined by …

Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The
localization and segmentation of IVDs are important for spine disease diagnosis and …

Stain colour estimation is a prominent factor of the analysis pipeline in most of histology
image processing algorithms. Providing a reliable and efficient stain colour deconvolution …

Detection of tumor nuclei in cancer histology images requires sophisticated techniques due
to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently …

Recently, deep learning frameworks have been shown to be successful and efficient in
processing digital histology images for various detection and classification tasks. Among …

Neoadjuvant treatment (NAT) of breast cancer (BCa) is an option for patients with the locally
advanced disease. It has been compared with standard adjuvant therapy with the aim of …

Automated nucleus/cell detection is usually considered as the basis and a critical
prerequisite step of computer assisted pathology and microscopy image analysis. However …