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20242026
most citedFoundation Models in Radiology: What, How, When, Why and Why Not

90 citations · 205 across the 22 of their papers we have counts for

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Showing 2024 · cs.CVShow all

5 papers · 2 filters

cs.CV2024

RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models

Maya Varma, Jean-Benoit Delbrouck, Zhihong Chen +2

Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time…

cs.CV2024★ 1 cited

CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback

Dennis Hein, Zhihong Chen, Sophie Ostmeier +8

Radiologists play a crucial role in translating medical images into actionable reports. However, the field faces staffing shortages and increasing workloads. While automated approa…

cs.CV2024

LieRE: Lie Rotational Positional Encodings

Sophie Ostmeier, Brian Axelrod, Maya Varma +3

Transformer architectures rely on position encodings to model the spatial structure of input data. Rotary Position Encoding (RoPE) is a widely used method in language models that e…

cs.CV2024★ 33 cited

Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset

Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37

The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…

cs.CV2024★ 26 cited

A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

Zhihong Chen, Maya Varma, Justin Xu +20

Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant…