Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Modeling dense multimodal interactions between biological pathways and histology for survival prediction
Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival
can improve our understanding of patient prognosis. However this multimodal task is …
can improve our understanding of patient prognosis. However this multimodal task is …
Gtp-4o: Modality-prompted heterogeneous graph learning for omni-modal biomedical representation
Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …
biomedical domains. While established techniques enable handling multi-modal …
Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology
Abstract Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract
discriminative feature representations within Whole Slide Images (WSIs) in computational …
discriminative feature representations within Whole Slide Images (WSIs) in computational …
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 …
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 …
Multi-omics based artificial intelligence for cancer research.
With significant advancements of next generation sequencing technologies, large amounts
of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and …
of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and …
A multimodal knowledge-enhanced whole-slide pathology foundation model
Remarkable strides in computational pathology have been made in the task-agnostic
foundation model that advances the performance of a wide array of downstream clinical …
foundation model that advances the performance of a wide array of downstream clinical …
Pathm3: A multimodal multi-task multiple instance learning framework for whole slide image classification and captioning
In the field of computational histopathology, both whole slide images (WSIs) and diagnostic
captions provide valuable insights for making diagnostic decisions. However, aligning WSIs …
captions provide valuable insights for making diagnostic decisions. However, aligning WSIs …
Towards a generalizable pathology foundation model via unified knowledge distillation
Foundation models pretrained on large-scale datasets are revolutionizing the field of
computational pathology (CPath). The generalization ability of foundation models is crucial …
computational pathology (CPath). The generalization ability of foundation models is crucial …