CoNNS uses an LLM-built concept ontology and cross-patient relabeling to filter noisy negatives, improving zero-shot classification and grounding of chest X-ray findings over prior methods.
In: International Conference on Medical Image Computing and Computer-Assisted Intervention
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
LoFi adds location-aware captioning loss to jointly optimize fine-grained representations, yielding better retrieval and grounding on MIMIC-CXR and PadChest-GR.
citing papers explorer
-
Concept-Guided Noisy Negative Suppression for Zero-Shot Classification and Grounding of Chest X-Ray Findings
CoNNS uses an LLM-built concept ontology and cross-patient relabeling to filter noisy negatives, improving zero-shot classification and grounding of chest X-ray findings over prior methods.
-
LoFi: Location-Aware Fine-Grained Representation Learning for Chest X-ray
LoFi adds location-aware captioning loss to jointly optimize fine-grained representations, yielding better retrieval and grounding on MIMIC-CXR and PadChest-GR.