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

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 framework that (i) reduces encoder sensitivity to domain shift via a \emph{Feature Fisher Information Penalty} (FIP) computed on 3D feature maps and (ii) penalizes overconfident voxel-wise errors through a \emph{Confidence Misalignment Penalty} (CMP). The combined loss, $\mathcal{L}_{\mathrm{CalSAM}}$ fine-tunes only the mask decoder while keeping SAM's encoders frozen. On cross-center and scanner-shift evaluations, CalSAM substantially improves accuracy and calibration: e.g., on the BraTS scanner split (Siemens$\to$GE) CalSAM shows a $+7.4\%$ relative improvement in $\mathrm{DSC}$ (80.1\% vs.\ 74.6\%), a $-26.9\%$ reduction in $\mathrm{HD95}$ (4.6 mm vs.\ 6.3 mm), and a $-39.5\%$ reduction in $\mathrm{ECE}$ (5.2\% vs.\ 8.6\%). On ATLAS-C (motion corruptions), CalSAM achieves a $+5.3\%$ relative improvement in $\mathrm{DSC}$ (75.9\%) and a $-32.6\%$ reduction in $\mathrm{ECE}$ (5.8\%). Ablations show FIP and CMP contribute complementary gains ($p<0.01$), and the Fisher penalty incurs a modest $\sim$15\% training-time overhead. CalSAM therefore delivers improved domain generalization and better-calibrated uncertainty estimates for brain MRI segmentation, while retaining the computational benefits of freezing SAM's encoder.

Authors 3

Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari

Specification

Official page: arxiv.org/abs/2509.23176
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2509.23176
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.CV
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2509.23176
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

2 h ago

Conflicts

None

No models linked to this paper yet.

Authors: Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · Published

1 claims · 1 propertiesShow all properties

Publishedpublished_at1

Claim history for Published
Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperConfidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
New paper: Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
arxiv
rss.arxiv.org

Full timeline →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	2 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.