Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations
with variable molecular profiles, aggressiveness, and proliferation potential coexist and …

Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …

Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …

Histopathological whole slide images (WSIs) classification has become a foundation task in
medical microscopic imaging processing. Prevailing approaches involve learning WSIs as …

Breast cancer is the most commonly diagnosed cancer and registers the highest number of
deaths for women. Advances in diagnostic activities combined with large-scale screening …

Abstract Large Vision-Language Models (LVLMs) are capable of handling diverse data
types such as imaging, text, and physiological signals, and can be applied in various fields …

Processing giga-pixel whole slide histopathology images (WSI) is a computationally
expensive task. Multiple instance learning (MIL) has become the conventional approach to …

Explainability of deep learning methods is imperative to facilitate their clinical adoption in
digital pathology. However, popular deep learning methods and explainability techniques …