Artificial intelligence for digital and computational pathology
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 …
including deep learning, have boosted the field of computational pathology. This field holds …
Quantification of tumor heterogeneity: from data acquisition to metric generation
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations
with variable molecular profiles, aggressiveness, and proliferation potential coexist and …
with variable molecular profiles, aggressiveness, and proliferation potential coexist and …
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …
been successful at capturing image representations but their use has been generally …
Visual language pretrained multiple instance zero-shot transfer for histopathology images
Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …
training new language-aware image encoders or augmenting existing pretrained models …
Bracs: A dataset for breast carcinoma subtyping in h&e histology images
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 …
deaths for women. Advances in diagnostic activities combined with large-scale screening …
Quantifying explainers of graph neural networks in computational pathology
Explainability of deep learning methods is imperative to facilitate their clinical adoption in
digital pathology. However, popular deep learning methods and explainability techniques …
digital pathology. However, popular deep learning methods and explainability techniques …
ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis
Whole slide image (WSI) analysis has become increasingly important in the medical
imaging community, enabling automated and objective diagnosis, prognosis, and …
imaging community, enabling automated and objective diagnosis, prognosis, and …
Differentiable zooming for multiple instance learning on whole-slide images
Abstract Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …
Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images
Processing giga-pixel whole slide histopathology images (WSI) is a computationally
expensive task. Multiple instance learning (MIL) has become the conventional approach to …
expensive task. Multiple instance learning (MIL) has become the conventional approach to …
A general-purpose self-supervised model for computational pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
objective characterizations of histopathologic biomarkers in anatomic pathology. However …