With foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification

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   Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification

   Marawan Elbatel, Hualiang Wang, Robert Martí et al. · 2023 · Computer Vision and Pattern Recognition · cited 11 times · AI summary available

   Federated aggregation addresses scanner- and population-related variation across medical-image clients, while this study tests unseen-hospital generalization for gastric slides.

   Abstract

   In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the inter-client intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a …

2. Close Shared data, tool or method Not relevant

   Virchow: A Million-Slide Digital Pathology Foundation Model

   Eugene Vorontsov, Alican Bozkurt, Adam Casson et al. · 2023 · Image and Video Processing · preprint, not peer reviewed · cited 164 times · AI summary available

   Virchow provides a self-supervised pathology foundation model for H&E slides, whereas this study evaluates classifiers on frozen pathology foundation-model features.

   Abstract

   The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer. Such applications will depend on models' abilities to capture the diverse patterns observed in pathology images. To address this challenge, we present Virchow, a foundation model for computational pathology. Using self-supervised learning empowered by the DINOv2 algorithm, Virchow is a vision transformer model with 632 million parameters trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue and specimen types, which is orders of magnitude more data t…

3. Close Shared data, tool or method Not relevant

   PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains

   Shengyi Hua, Fang Yan, Tianle Shen et al. · 2023 · Computer Vision and Pattern Recognition · preprint, not peer reviewed · cited 42 times · AI summary available

   PathoDuet develops pretrained pathology models for H&E and IHC slides, while this study uses frozen pathology foundation-model features for gastric classification.

   Abstract

   Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and …

4. Close Another approach to the problem Not relevant

   Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning

   Jiawei Yang, Hanbo Chen, Jiangpeng Yan et al. · 2022 · Image and Video Processing · cited 28 times · AI summary available

   The few-shot study uses contrastive learning and latent augmentation for cross-domain histology classification, while this study evaluates cross-hospital gastric classification with frozen features.

   Abstract

   Few-shot learning is an established topic in natural images for years, but few work is attended to histology images, which is of high clinical value since well-labeled datasets and rare abnormal samples are expensive to collect. Here, we facilitate the study of few-shot learning in histology images by setting up three cross-domain tasks that simulate real clinics problems. To enable label-efficient learning and better generalizability, we propose to incorporate contrastive learning (CL) with latent augmentation (LA) to build a few-shot system. CL learns useful representations without manual labels, while LA transfers semantic variations of the base dataset in an unsupervised way. These two c…

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   Combining Insights From Multiple Large Language Models Improves Diagnostic Accuracy

   Gioele Barabucci, Victor Shia, Eugene Chu et al. · 2024 · Artificial Intelligence · cited 7 times · AI summary available

   This study aggregates language-model diagnostic responses on clinical vignettes, whereas the gastric slide study compares multiple-instance learning aggregators for image classification.

   Abstract

   Background: Large language models (LLMs) such as OpenAI's GPT-4 or Google's PaLM 2 are proposed as viable diagnostic support tools or even spoken of as replacements for "curbside consults". However, even LLMs specifically trained on medical topics may lack sufficient diagnostic accuracy for real-life applications. Methods: Using collective intelligence methods and a dataset of 200 clinical vignettes of real-life cases, we assessed and compared the accuracy of differential diagnoses obtained by asking individual commercial LLMs (OpenAI GPT-4, Google PaLM 2, Cohere Command, Meta Llama 2) against the accuracy of differential diagnoses synthesized by aggregating responses from combinations of th…

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   Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?

   Ziqin Lin, Heng Li, Zinan Li et al. · 2024 · Image and Video Processing · preprint, not peer reviewed · cited 1 time · AI summary available

   This study examines dataset quality and bias in fundus diagnosis using a foundation model, while the gastric study tests unseen-hospital discrimination with pathology features.

   Abstract

   Recent advancements in pre-trained large foundation models (LFM) have yielded significant breakthroughs across various domains, including natural language processing and computer vision. These models have been particularly impactful in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM has been developed for fundus images using the Vision Transformer (VIT) and a self-supervised learning framework. This LFM has shown promising performance in fundus disease diagnosis across multiple datasets. On the other hand, deep learning models have long been challenged by dataset quality issues, such as image quality and dataset bias. To investigate the influence of data quality …

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   Customizing General-Purpose Foundation Models for Medical Report Generation

   Bang Yang, Asif Raza, Yuexian Zou et al. · 2023 · Computer Vision and Pattern Recognition · preprint, not peer reviewed · cited 15 times · AI summary available

   This study customizes general-purpose foundation models for medical report generation, whereas the gastric study uses pathology-model features for slide classification.

   Abstract

   Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given medical images. However, the scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks capable of harnessing the potential artificial general intelligence power like large language models (LLMs). In this work, we propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs), in computer vision and natural language processing with a specific focus on medical report generation. Specifically, follo…

8. Related Another approach to the problem Not relevant

   StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification

   Pietro Antonio Cicalese, Aryan Mobiny, Pengyu Yuan et al. · 2020 · Computer Vision and Pattern Recognition · cited 19 times · AI summary available

   StyPath uses stain-style transfer augmentation to improve kidney histology classification robustness, while this study tests feature alignment and aggregation across hospitals.

   Abstract

   The classification of Antibody Mediated Rejection (AMR) in kidney transplant remains challenging even for experienced nephropathologists; this is partly because histological tissue stain analysis is often characterized by low inter-observer agreement and poor reproducibility. One of the implicated causes for inter-observer disagreement is the variability of tissue stain quality between (and within) pathology labs, coupled with the gradual fading of archival sections. Variations in stain colors and intensities can make tissue evaluation difficult for pathologists, ultimately affecting their ability to describe relevant morphological features. Being able to accurately predict the AMR status ba…