Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
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 …
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 …
Morphological prototyping for unsupervised slide representation learning in computational pathology
Abstract Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …
Weakly supervised joint whole-slide segmentation and classification in prostate cancer
The identification and segmentation of histological regions of interest can provide significant
support to pathologists in their diagnostic tasks. However, segmentation methods are …
support to pathologists in their diagnostic tasks. However, segmentation methods are …
Transcriptomics-guided slide representation learning in computational pathology
G Jaume, L Oldenburg, A Vaidya… - Proceedings of the …, 2024 - openaccess.thecvf.com
Self-supervised learning (SSL) has been successful in building patch embeddings of small
histology images (eg 224 x 224 pixels) but scaling these models to learn slide embeddings …
histology images (eg 224 x 224 pixels) but scaling these models to learn slide embeddings …
Machine learning in computational histopathology: Challenges and opportunities
Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …
Retrieval-augmented multiple instance learning
Abstract Multiple Instance Learning (MIL) is a crucial weakly supervised learning method
applied across various domains, eg, medical diagnosis based on whole slide images …
applied across various domains, eg, medical diagnosis based on whole slide images …
The rise of ai language pathologists: Exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification
This paper introduces the novel concept of few-shot weakly supervised learning for
pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed …
pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed …
Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole-Slide Image Classification
In computational pathology, whole-slide image (WSI) classification presents a formidable
challenge due to its gigapixel resolution and limited fine-grained annotations. Multiple …
challenge due to its gigapixel resolution and limited fine-grained annotations. Multiple …