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CalSAM enhances Segment Anything Model for brain MRI segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CalSAM, a new framework designed to improve the accuracy and reliability of the Segment Anything Model (SAM) when applied to brain MRI segmentation. CalSAM addresses domain shift issues and overconfidence in SAM's predictions by incorporating a Feature Fisher Information Penalty (FIP) and a Confidence Misalignment Penalty (CMP). This approach, which fine-tunes only the mask decoder while keeping SAM's encoders frozen, has shown significant improvements in Dice Similarity Coefficient (DSC) and Hausdorff Distance 95 (HD95) on various scanner and corruption shifts, while also reducing calibration error (ECE). AI

IMPACT Improves robustness and calibration of foundation models for medical imaging tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for improving an existing model's performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CalSAM enhances Segment Anything Model for brain MRI segmentation

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The cluster contains an academic paper detailing a new method for improving an existing model's performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari, Vasile Palade, Vincent Vigneron ·

    Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift

    arXiv:2509.23176v2 Announce Type: replace Abstract: The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when applied to medical volumes. We propose \textbf{CalSAM}, a lightweight adaptation fram…

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