A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification DOI Creative Commons
Jack Breen, Katrina J. Allen, Kieran Zucker

et al.

npj Precision Oncology, Journal Year: 2025, Volume and Issue: 9(1)

Published: Jan. 30, 2025

Language: Английский

A whole-slide foundation model for digital pathology from real-world data DOI Creative Commons
Hanwen Xu, Naoto Usuyama,

Jaspreet Bagga

et al.

Nature, Journal Year: 2024, Volume and Issue: 630(8015), P. 181 - 188

Published: May 22, 2024

Abstract Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands image tiles 1–3 . Prior models have often resorted to subsampling small portion for each slide, thus missing the important slide-level context 4 Here we present Prov-GigaPath, whole-slide foundation model pretrained on 1.3 billion 256 × in 171,189 whole slides from Providence, large US health network comprising 28 cancer centres. The originated more than 30,000 patients covering 31 major tissue types. To pretrain propose GigaPath, novel vision transformer architecture pretraining slides. scale GigaPath learning with tiles, adapts newly developed LongNet 5 method digital pathology. evaluate construct benchmark 9 subtyping tasks and 17 pathomics tasks, using both Providence TCGA data 6 With large-scale ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance 25 out 26 significant improvement over second-best 18 tasks. We further demonstrate potential vision–language 7,8 by incorporating reports. In sum, is an open-weight that achieves various demonstrating importance real-world modelling.

Language: Английский

Citations

119
A Multimodal Generative AI Copilot for Human Pathology DOI Creative Commons
Ming Y. Lu, Bowen Chen, Drew F. K. Williamson

et al.

Nature, Journal Year: 2024, Volume and Issue: unknown

Published: June 12, 2024

Computational pathology

Language: Английский

Citations

87
A guide to artificial intelligence for cancer researchers DOI
Raquel Pérez-López, Narmin Ghaffari Laleh, Faisal Mahmood

et al.

Nature reviews. Cancer, Journal Year: 2024, Volume and Issue: 24(6), P. 427 - 441

Published: May 16, 2024

Language: Английский

Citations

75
A foundation model for clinical-grade computational pathology and rare cancers detection DOI Creative Commons
Eugene Vorontsov, Alican Bozkurt, Adam Casson

et al.

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(10), P. 2924 - 2935

Published: July 22, 2024

Abstract The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. success such applications depends on the ability model diverse patterns observed in pathology images. To this end, we present Virchow, largest foundation for computational date. In addition evaluation biomarker prediction cell identification, demonstrate that a large enables pan-cancer detection, achieving 0.95 specimen-level area under (receiver operating characteristic) curve across nine common seven rare cancers. Furthermore, show less training data, detector built Virchow can achieve similar performance tissue-specific clinical-grade models production outperform them some variants cancer. Virchow’s gains highlight value open possibilities many high-impact limited amounts labeled data.

Language: Английский

Citations

65
A pathology foundation model for cancer diagnosis and prognosis prediction DOI
Xiyue Wang, Junhan Zhao, Eliana Marostica

et al.

Nature, Journal Year: 2024, Volume and Issue: 634(8035), P. 970 - 978

Published: Sept. 4, 2024

Language: Английский

Citations

60
Analysis of 3D pathology samples using weakly supervised AI DOI Creative Commons
Andrew H. Song, Mane Williams, Drew F. K. Williamson

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(10), P. 2502 - 2520.e17

Published: May 1, 2024

Human tissue, which is inherently three-dimensional (3D), traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation hampered by complex manual evaluation and lack of computational platforms distill insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing volumes efficiently predicting outcomes based on morphological features. Recurrence risk-stratification models were trained prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing morphologies, volume-based prognostication achieves superior performance traditional 2D slice-based approaches, including clinical/histopathological baselines six certified genitourinary pathologists. Incorporating greater volume improves prognostic mitigates risk prediction variability bias, further emphasizing value larger extents heterogeneous morphology.

Language: Английский

Citations

24
Demographic bias in misdiagnosis by computational pathology models DOI
Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson

et al.

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(4), P. 1174 - 1190

Published: April 1, 2024

Language: Английский

Citations

23
How to build the virtual cell with artificial intelligence: Priorities and opportunities DOI Creative Commons
Charlotte Bunne, Yusuf Roohani, Yanay Rosen

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(25), P. 7045 - 7063

Published: Dec. 1, 2024

Cells are essential to understanding health and disease, yet traditional models fall short of modeling simulating their function behavior. Advances in AI omics offer groundbreaking opportunities create an virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent simulate the behavior molecules, cells, tissues across diverse states. This Perspective provides vision on design how collaborative efforts build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, guiding experimental studies, offering new for cellular functions fostering interdisciplinary collaborations open science.

Language: Английский

Citations

22
Vision-language models for medical report generation and visual question answering: a review DOI Creative Commons
Iryna Hartsock, Ghulam Rasool

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: Nov. 19, 2024

Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on publicly available designed report generation question answering (VQA). We provide background NLP CV, explaining how techniques from both fields are integrated into VLMs, with data often fused using Transformer-based architectures enable effective learning multimodal Key areas we address include the exploration of 18 public datasets, in-depth analyses pre-training strategies 16 noteworthy comprehensive discussion evaluation metrics assessing VLMs' performance VQA. also highlight current challenges facing VLM development, including limited availability, concerns privacy, lack proper metrics, among others, while proposing future directions these obstacles. Overall, our review summarizes progress harness improved healthcare applications.

Language: Английский

Citations

21
A vision–language foundation model for precision oncology DOI
Jinxi Xiang, Xiyue Wang, Xiaoming Zhang

et al.

Nature, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Language: Английский

Citations

9