A guide to artificial intelligence for cancer researchers
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …
a readily accessible tool for cancer researchers. AI-based tools can boost research …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Task-specific deep learning models in histopathology offer promising opportunities for
improving diagnosis, clinical research, and precision medicine. However, development of …
improving diagnosis, clinical research, and precision medicine. However, development of …
Computational Pathology at Health System Scale--Self-Supervised Foundation Models from Three Billion Images
G Campanella, R Kwan, E Fluder, J Zeng… - arXiv preprint arXiv …, 2023 - arxiv.org
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled
datasets to train visual foundation models that can generalize to a variety of downstream …
datasets to train visual foundation models that can generalize to a variety of downstream …
Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types
As vast histological archives are digitised, there is a pressing need to be able to associate
specific tissue substructures and incident pathology to disease outcomes without arduous …
specific tissue substructures and incident pathology to disease outcomes without arduous …
Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine
Simple Summary Technological and scientific innovations, from genetic sequencing to
digital pathology slide scanners, have drastically altered the field of neuropathology. The …
digital pathology slide scanners, have drastically altered the field of neuropathology. The …
Domain generalization in computational pathology: survey and guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
In-context learning enables multimodal large language models to classify cancer pathology images
D Ferber, G Wölflein, IC Wiest, M Ligero… - arXiv preprint arXiv …, 2024 - arxiv.org
Medical image classification requires labeled, task-specific datasets which are used to train
deep learning networks de novo, or to fine-tune foundation models. However, this process is …
deep learning networks de novo, or to fine-tune foundation models. However, this process is …
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
Pre-training datasets, like ImageNet, have become the gold standard in medical image
analysis. However, the emergence of self-supervised learning (SSL), which leverages …
analysis. However, the emergence of self-supervised learning (SSL), which leverages …
Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images
Background Pretraining labeled datasets, like ImageNet, have become a technical standard
in advanced medical image analysis. However, the emergence of self-supervised learning …
in advanced medical image analysis. However, the emergence of self-supervised learning …