Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Abstract Domain Adaptation (DA) has recently been of strong interest in the medical imaging
community. While a large variety of DA techniques have been proposed for image …

Abstract Domain adaptation (DA) has drawn high interests for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …

We propose a novel weakly supervised learning segmentation based on several global
constraints derived from box annotations. Particularly, we leverage a classical tightness prior …

Using deep learning models to diagnose cancer from histology data presents several
challenges. Cancer grading and localization of regions of interest (ROIs) in these images …

Automated cellular instance segmentation is a process utilized for accelerating biological
research for the past two decades, and recent advancements have produced higher quality …

Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates
the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-level …

Precise determination and assessment of bladder cancer (BC) extent of muscle invasion
involvement guides proper risk stratification and personalized therapy selection. In this …

CNN visualization and interpretation methods, like class-activation maps (CAMs), are
typically used to highlight the image regions linked to class predictions. These models allow …