The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …

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

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …

Automatic detection and segmentation of cells and nuclei in microscopy images is important
for many biological applications. Recent successful learning-based approaches include per …

Semantic segmentation is a challenging task in the absence of densely labelled data. Only
relying on class activation maps (CAM) with image-level labels provides deficient …

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-
art performance in the last few years. More specifically, these techniques have been …