Public articles linked to the same research event.
arXiv The work presents AbSteering, a two-stage framework combining abnormality-centric chain-of-thought training with a Direct Preference Optimization objective for fine-grained abnormality discrimination, to adapt general-purpose VideoLMs to high-resolution CT report generation, and curates the CT-RATE-AB dataset; results show that general-purpose VideoLMs transfer effectively to 3D medical imaging under limited data, achieving state-of-the-art performance on fine-grained clinical efficacy metrics, with superior detection sensitivity over domain-specific CT foundation models pretrained on large-scale CTs while mitigating hallucinations.
The work presents AbSteering, a two-stage framework combining abnormality-centric chain-of-thought training with a Direct Preference Optimization objective for fine-grained abnormality discrimination, to adapt general-purpose VideoLMs to high-resolution CT report generation, and curates the CT-RATE-AB dataset; results show that general-purpose VideoLMs transfer effectively to 3D medical imaging under limited data, achieving state-of-the-art performance on fine-grained clinical efficacy metrics, with superior detection sensitivity over domain-specific CT foundation models pretrained on large-scale CTs while mitigating hallucinations.
The work presents AbSteering, a two-stage framework combining abnormality-centric chain-of-thought training with a Direct Preference Optimization objective for fine-grained abnormality discrimination, to adapt general-purpose VideoLMs to high-resolution CT report generation, and curates the CT-RATE-AB dataset; results show that general-purpose VideoLMs transfer effectively to 3D medical imaging under limited data, achieving state-of-the-art performance on fine-grained clinical efficacy metrics, with superior detection sensitivity over domain-specific CT foundation models pretrained on large-scale CTs while mitigating hallucinations.
The work presents AbSteering, a two-stage framework combining abnormality-centric chain-of-thought training with a Direct Preference Optimization objective for fine-grained abnormality discrimination, to adapt general-purpose VideoLMs to high-resolution CT report generation, and curates the CT-RATE-AB dataset; results show that general-purpose VideoLMs transfer effectively to 3D medical imaging under limited data, achieving state-of-the-art performance on fine-grained clinical efficacy metrics, with superior detection sensitivity over domain-specific CT foundation models pretrained on large-scale CTs while mitigating hallucinations.