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: European conference on computer vision
6 Pith papers cite this work. Polarity classification is still indexing.
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A teacher–student semi-supervised framework with alignment-preserving patch mixing, position-aware text augmentation, and positional contrastive learning improves medical referring segmentation at low label ratios.
SemEnrich enriches radiology reports with positive/neutral findings via self-supervised semantic clustering, yielding average gains of 5-7% on COMET, BERT score, Sentence BLEU, CheXbert-F1 and RadGraph-F1 after fine-tuning, plus further gains when cluster info is added to GRPO rewards.
Frozen CT-CLIP representations plus a lightweight DeepSurv head outperform CoxPH and match or beat other multimodal baselines for lung-cancer survival on a 242-patient real-world cohort.
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.
KEPIL integrates medical ontologies and a semantic contrastive loss into vision-language models to achieve state-of-the-art prompt-robust zero-shot disease detection in radiology, with reported AUC gains of 6.37% on CheXpert under prompt variations.
citing papers explorer
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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.
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Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
A teacher–student semi-supervised framework with alignment-preserving patch mixing, position-aware text augmentation, and positional contrastive learning improves medical referring segmentation at low label ratios.
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SemEnrich: Self-Supervised Semantic Enrichment of Radiology Reports for Vision-Language Learning
SemEnrich enriches radiology reports with positive/neutral findings via self-supervised semantic clustering, yielding average gains of 5-7% on COMET, BERT score, Sentence BLEU, CheXbert-F1 and RadGraph-F1 after fine-tuning, plus further gains when cluster info is added to GRPO rewards.
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CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
Frozen CT-CLIP representations plus a lightweight DeepSurv head outperform CoxPH and match or beat other multimodal baselines for lung-cancer survival on a 242-patient real-world cohort.
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CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.
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KEPIL: Knowledge-Enhanced Prompt-Image Learning for Prompt-Robust Disease Detection
KEPIL integrates medical ontologies and a semantic contrastive loss into vision-language models to achieve state-of-the-art prompt-robust zero-shot disease detection in radiology, with reported AUC gains of 6.37% on CheXpert under prompt variations.