Region-Grounded Vision-Language Learning for Detection-Guided Mammographic Lesion Classification
Ranking
Overall
75
Content
90
Popularity
41
Observed public metrics from 1 member.
Merged summary
TL;DR - A region-grounded vision-language method for mammographic lesion classification that aligns localized lesion features with clinical text descriptors, mirroring how radiologists work. It matters because standard whole-image contrastive alignment dilutes the small, subtle lesion cues critical for malignancy assessment.
- Region-text contrastive pretraining aligns lesion-specific features with structured clinical descriptors from radiology metadata, rather than aligning at the whole-image level.
- Multi-component objective combats semantic collapse and background bias in low-vocabulary settings via positive alignment, fine-grained semantic hard negatives, and background suppression.
- Auxiliary lesion detection head is jointly optimized with contrastive classification to preserve spatial sensitivity and enable localization-aware malignancy classification.
- Evaluated on CBIS-DDSM and VinDr-Mammo, reporting superior performance vs. related methods across in-domain, cross-dataset, and transfer-learning settings (specific metrics not provided).
Sources (1)
Region-Grounded Vision-Language Learning for Detection-Guided Mammographic Lesion Classification
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - A region-grounded vision-language method for mammographic lesion classification that aligns localized lesion features with clinical text descriptors, mirroring how radiologists work. It matters because standard whole-image contrastive alignment dilutes the small, subtle lesion cues critical for malignancy assessment.
- Region-text contrastive pretraining aligns lesion-specific features with structured clinical descriptors from radiology metadata, rather than aligning at the whole-image level.
- Multi-component objective combats semantic collapse and background bias in low-vocabulary settings via positive alignment, fine-grained semantic hard negatives, and background suppression.
- Auxiliary lesion detection head is jointly optimized with contrastive classification to preserve spatial sensitivity and enable localization-aware malignancy classification.
- Evaluated on CBIS-DDSM and VinDr-Mammo, reporting superior performance vs. related methods across in-domain, cross-dataset, and transfer-learning settings (specific metrics not provided).