Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography

• URL: http://arxiv.org/abs/2506.13964v1
• Date: Mon, 16 Jun 2025 20:14:37 GMT
• Title: Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
• Authors: Yusdivia Molina-Román, David Gómez-Ortiz, Ernestina Menasalvas-Ruiz, José Gerardo Tamez-Peña, Alejandro Santos-Díaz,
• Abstract summary: This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system.<n>Zero-shot classification achieved modest performance, while the fine-tuned ConvNeXt model outperformed the BioMedCLIP linear probe.<n>These findings suggest that despite the promise of multimodal learning, CNN-based models with end-to-end fine-tuning provide stronger performance for specialized medical imaging.
• Score: 39.58317527488534
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system, evaluating BioMedCLIP and ConvNeXt across three learning scenarios: zero-shot classification, linear probing with textual descriptions, and fine-tuning with numerical labels. Results show that zero-shot classification achieved modest performance, while the fine-tuned ConvNeXt model outperformed the BioMedCLIP linear probe. Although linear probing demonstrated potential with pretrained embeddings, it was less effective than full fine-tuning. These findings suggest that despite the promise of multimodal learning, CNN-based models with end-to-end fine-tuning provide stronger performance for specialized medical imaging. The study underscores the need for more detailed textual representations and domain-specific adaptations in future radiology applications.

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