E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation

• URL: http://arxiv.org/abs/2508.17408v1
• Date: Sun, 24 Aug 2025 15:29:21 GMT
• Title: E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation
• Abstract summary: E-BayesSAM is an efficient framework combining Token-wise Variational Bayesian Inference (T-VBI) for efficienty Bayesian adaptation and Self- optimizing Kolmogorov-Arnold Network (SO-KAN) for improving interpretability.<n>By unifying efficiency, reliability, and interpretability, E-BayesSAM bridges SAM's versatility with clinical needs, advancing deployment in safety-critical medical applications.
• Score: 14.052832670347778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the Segment Anything Model (SAM) has advanced medical image segmentation, its Bayesian adaptation for uncertainty-aware segmentation remains hindered by three key issues: (1) instability in Bayesian fine-tuning of large pre-trained SAMs; (2) high computation cost due to SAM's massive parameters; (3) SAM's black-box design limits interpretability. To overcome these, we propose E-BayesSAM, an efficient framework combining Token-wise Variational Bayesian Inference (T-VBI) for efficienty Bayesian adaptation and Self-Optimizing Kolmogorov-Arnold Network (SO-KAN) for improving interpretability. T-VBI innovatively reinterprets SAM's output tokens as dynamic probabilistic weights and reparameterizes them as latent variables without auxiliary training, enabling training-free VBI for uncertainty estimation. SO-KAN improves token prediction with learnable spline activations via self-supervised learning, providing insight to prune redundant tokens to boost efficiency and accuracy. Experiments on five ultrasound datasets demonstrated that E-BayesSAM achieves: (i) real-time inference (0.03s/image), (ii) superior segmentation accuracy (average DSC: Pruned E-BayesSAM's 89.0\% vs. E-BayesSAM's 88.0% vs. MedSAM's 88.3%), and (iii) identification of four critical tokens governing SAM's decisions. By unifying efficiency, reliability, and interpretability, E-BayesSAM bridges SAM's versatility with clinical needs, advancing deployment in safety-critical medical applications. The source code is available at https://github.com/mp31192/E-BayesSAM.

Related papers

• EOVSAM: Efficient Open-Vocabulary Segmentation with SAM 3 in One Pass [79.69317943751223]
  We propose an Efficient Open-Vocabulary segmentation framework with SAM 3 (EOVSAM)<n>EOVSAM adapts SAM 3 for single-pass prediction.<n>It consistently improves segmentation accuracy over vanilla SAM 3 on all evaluated datasets.
  arXiv  Detail & Related papers  (2026-08-03T14:15:37Z)
• VesSAM: Efficient Multi-Prompting for Segmenting Complex Vessel [68.24765319399286]
  We present VesSAM, a powerful and efficient framework tailored for 2D vessel segmentation.<n>VesSAM integrates (1) a convolutional adapter to enhance local texture features, (2) a multi-prompt encoder that fuses anatomical prompts, and (3) a lightweight mask decoder to reduce jagged artifacts.<n>VesSAM consistently outperforms state-of-the-art PEFT-based SAM variants by over 10% Dice and 13% IoU.
  arXiv  Detail & Related papers  (2025-11-02T15:47:05Z)
• Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification [113.6840565194525]
  Real-world datasets often follow a long-tailed distribution, making generalization to tail classes difficult.<n>Recent methods resorted to long-tail variants of Sharpness-Aware Minimization (SAM) to improve generalization by flattening the loss landscape.<n>We introduce Focal-SAM, which assigns different penalties to class-wise, achieving fine-grained control without extra backpropagations.
  arXiv  Detail & Related papers  (2025-05-03T03:01:28Z)
• SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model [9.381558154295012]
  We propose Perceptual-Consistency Clipping, which exploits attention focus overlap as clipping metric, to significantly suppress outliers.<n>We also propose Prompt-Aware Reconstruction, which incorporates visual-prompt interactions by leveraging cross-attention responses in mask decoder.<n>Our method achieves 11.7% higher mAP than the baseline in segmentation task.
  arXiv  Detail & Related papers  (2025-03-09T08:38:32Z)
• RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 [15.50695315680438]
  Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecessor.<n>However, similar to SAM, SAM 2 is limited by its output of binary masks, inability to infer semantic labels, and dependence on precise prompts for the target object area.<n>We explore the upper performance limit of SAM 2 using custom fine-tuning adapters, achieving a Dice Similarity Coefficient (DSC) of 92.30% on the BTCV dataset.
  arXiv  Detail & Related papers  (2025-02-04T22:03:23Z)
• Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning [63.55145330447408]
  We propose a novel textbfSelf-textbfPerceptinon textbfTuning (textbfSPT) method for anomaly segmentation.<n>The SPT method incorporates a self-drafting tuning strategy, which generates an initial coarse draft of the anomaly mask, followed by a refinement process.
  arXiv  Detail & Related papers  (2024-11-26T08:33:25Z)
• SAMPa: Sharpness-aware Minimization Parallelized [51.668052890249726]
  Sharpness-aware (SAM) has been shown to improve the generalization of neural networks. Each SAM update requires emphsequentially computing two gradients, effectively doubling the per-iteration cost. We propose a simple modification of SAM, termed SAMPa, which allows us to fully parallelize the two gradient computations.
  arXiv  Detail & Related papers  (2024-10-14T16:21:23Z)
• PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images [11.373941923130305]
  PAM (Propagating Anything Model) is a segmentation approach that uses a 2D prompt, like a bounding box or sketch, to create a complete 3D segmentation of medical image volumes. It significantly outperformed existing models like MedSAM and SegVol, with an average improvement of over 18.1% in dice similarity coefficient (DSC) across 44 medical datasets and various object types.
  arXiv  Detail & Related papers  (2024-08-25T13:42:47Z)
• Stable Segment Anything Model [79.9005670886038]
  The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts. This paper presents the first comprehensive analysis on SAM's segmentation stability across a diverse spectrum of prompt qualities. Our solution, termed Stable-SAM, offers several advantages: 1) improved SAM's segmentation stability across a wide range of prompt qualities, while 2) retaining SAM's powerful promptable segmentation efficiency and generality.
  arXiv  Detail & Related papers  (2023-11-27T12:51:42Z)
• SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image [29.063412868961098]
  We propose multi-box prompts triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. Our experimental results found that multi-box prompts augmentation improve the SAM performance, and endowed each pixel with uncertainty.
  arXiv  Detail & Related papers  (2023-07-11T02:27:45Z)
• How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images [15.181219203629643]
  Segment Anything (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images. However, when applied to medical images, SAM suffers from noticeable performance drop. In this work, we propose to freeze SAM encoder and finetune a lightweight task-specific prediction head.
  arXiv  Detail & Related papers  (2023-06-23T18:34:30Z)
• Efficient Sharpness-aware Minimization for Improved Training of Neural Networks [146.2011175973769]
  This paper proposes Efficient Sharpness Aware Minimizer (M) which boosts SAM s efficiency at no cost to its generalization performance. M includes two novel and efficient training strategies-StochasticWeight Perturbation and Sharpness-Sensitive Data Selection. We show, via extensive experiments on the CIFAR and ImageNet datasets, that ESAM enhances the efficiency over SAM from requiring 100% extra computations to 40% vis-a-vis bases.
  arXiv  Detail & Related papers  (2021-10-07T02:20:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.