CheXTemporal supplies paired chest X-rays with explicit temporal progression taxonomy and spatial grounding to benchmark and improve models on longitudinal reasoning tasks.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
fields
cs.CV 4representative citing papers
TILA uses temporal inversion of image pairs as a supervisory signal to make existing temporal vision-language models more sensitive to directional interval changes in chest X-rays.
APO framework aligns multi-source MLLM reasoning under concept drift by using inter-model divergences as negative constraints via supervised bootstrapping and multi-negative Plackett-Luce optimization, with a 7B model outperforming proprietary sources on chest X-ray tasks and a new CXR-MAX benchmark
M4CXR is a multi-modal large language model that performs multiple tasks in chest X-ray analysis including report generation with claimed SOTA clinical accuracy using chain-of-thought prompting.
citing papers explorer
-
CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography
CheXTemporal supplies paired chest X-rays with explicit temporal progression taxonomy and spatial grounding to benchmark and improve models on longitudinal reasoning tasks.
-
Temporal Inversion for Learning Interval Change in Chest X-Rays
TILA uses temporal inversion of image pairs as a supervisory signal to make existing temporal vision-language models more sensitive to directional interval changes in chest X-rays.
-
Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Multi-Stream Environments
APO framework aligns multi-source MLLM reasoning under concept drift by using inter-model divergences as negative constraints via supervised bootstrapping and multi-negative Plackett-Luce optimization, with a 7B model outperforming proprietary sources on chest X-ray tasks and a new CXR-MAX benchmark
-
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation
M4CXR is a multi-modal large language model that performs multiple tasks in chest X-ray analysis including report generation with claimed SOTA clinical accuracy using chain-of-thought prompting.