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 …
An intentional approach to managing bias in general purpose embedding models
Advances in machine learning for health care have brought concerns about bias from the
research community; specifically, the introduction, perpetuation, or exacerbation of care …
research community; specifically, the introduction, perpetuation, or exacerbation of care …
Risk of bias in chest radiography deep learning foundation models
Purpose To analyze a recently published chest radiography foundation model for the
presence of biases that could lead to subgroup performance disparities across biologic sex …
presence of biases that could lead to subgroup performance disparities across biologic sex …
Strategies for implementing machine learning algorithms in the clinical practice of radiology
Despite recent advancements in machine learning (ML) applications in health care, there
have been few benefits and improvements to clinical medicine in the hospital setting. To …
have been few benefits and improvements to clinical medicine in the hospital setting. To …
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
In this work, we present an approach, which we call Embeddings for Language/Image-
aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or …
aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or …
Maira-1: A specialised large multimodal model for radiology report generation
We present a radiology-specific multimodal model for the task for generating radiological
reports from chest X-rays (CXRs). Our work builds on the idea that large language model (s) …
reports from chest X-rays (CXRs). Our work builds on the idea that large language model (s) …
Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study
YD Cid, M Macpherson, L Gervais-Andre… - The Lancet Digital …, 2024 - thelancet.com
Background Artificial intelligence (AI) systems for automated chest x-ray interpretation hold
promise for standardising reporting and reducing delays in health systems with shortages of …
promise for standardising reporting and reducing delays in health systems with shortages of …
ACTIS: Improving data efficiency by leveraging semi-supervised Augmentation Consistency Training for Instance Segmentation
Segmenting objects like cells or nuclei in biomedical microscopy data is a standard task
required for many downstream analyses. However, existing pre-trained models are …
required for many downstream analyses. However, existing pre-trained models are …
No filter: Cultural and socioeconomic diversityin contrastive vision-language models
We study cultural and socioeconomic diversity in contrastive vision-language models
(VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to …
(VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to …
Using generative AI to investigate medical imagery models and datasets
Background AI models have shown promise in performing many medical imaging tasks.
However, our ability to explain what signals these models have learned is severely lacking …
However, our ability to explain what signals these models have learned is severely lacking …