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20242026
most citedMammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting

1 citations · 1 across the 1 of their papers we have counts for

collaborators

8 papers

cs.CV20261 cited

Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting

Shantanu Ghosh, Vedant Parthesh Joshi, Rayan Syed +14

Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest…

cs.CV2025

Feature Quality and Adaptability of Medical Foundation Models: A Comparative Evaluation for Radiographic Classification and Segmentation

Frank Li, Theo Dapamede, Mohammadreza Chavoshi +12

Foundation models (FMs) promise to generalize medical imaging, but their effectiveness varies. It remains unclear how pre-training domain (medical vs. general), paradigm (e.g., tex…

stat.ME2025

Impact of Label Noise from Large Language Models Generated Annotations on Evaluation of Diagnostic Model Performance

Mohammadreza Chavoshi, Hari Trivedi, Janice Newsome +6

Large language models (LLMs) are increasingly used to generate labels from radiology reports to enable large-scale AI evaluation. However, label noise from LLMs can introduce bias…

cs.CV2025

Evaluating Vision Language Models (VLMs) for Radiology: A Comprehensive Analysis

Frank Li, Hari Trivedi, Bardia Khosravi +8

Foundation models, trained on vast amounts of data using self-supervised techniques, have emerged as a promising frontier for advancing artificial intelligence (AI) applications in…

eess.IV2025

A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study

Jungkyu Park, Jan Witowski, Yanqi Xu +8

Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer scr…

eess.IV2025

Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk

Theodorus Dapamede, Aisha Urooj, Vedant Joshi +15

Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk…