Daniel F Schmidt | Searcharxiv

arXiv2025

SAM-aware Test-time Adaptation for Universal Medical Image Segmentation

Leveraging the Segment Anything Model (SAM) for medical image segmentation remains challenging due to its limited adaptability across diverse medical domains. Although fine-tuned variants, such as MedSAM, improve performance in scenarios similar to the training modalities or organs, they may lack generalizability to unseen data. To overcome this limitation, we propose SAM-aware Test-time Adaptation (SAM-TTA), a lightweight and flexible framework that preserves SAM's inherent generalization ability while enhancing segmentation accuracy for medical images. SAM-TTA tackles two major challenges: (1) input-level discrepancy caused by channel mismatches between natural and medical images, and (2) semantic-level discrepancy due to different object characteristics in natural versus medical images (e.g., with clear boundaries vs. ambiguous structures). To this end, we introduce two complementary components: a self-adaptive Bezier Curve-based Transformation (SBCT), which maps single-channel medical images into SAM-compatible three-channel images via a few learnable parameters to be optimized at test time; and IoU-guided Multi-scale Adaptation (IMA), which leverages SAM's intrinsic IoU scores to enforce high output confidence, dual-scale prediction consistency, and intermediate feature consistency, to improve semantic-level alignments. Extensive experiments on eight public medical image segmentation tasks, covering six grayscale and two color (endoscopic) tasks, demonstrate that SAM-TTA consistently outperforms state-of-the-art test-time adaptation methods. Notably, on six grayscale datasets, SAM-TTA even surpasses fully fine-tuned models, achieving significant Dice improvements (i.e., average 4.8% and 7.4% gains over MedSAM and SAM-Med2D) and establishing a new paradigm for universal medical image segmentation. Code is available at https://github.com/JianghaoWu/SAM-TTA.

cs.CV↗

arXiv2023

From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery

Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-informed acquisition function--with the goal to maximize a certain "reward" over time, such as the maximum property value discovered so far. While the materials science community achieved much progress in developing property models that predict well on average with respect to the training distribution, this form of in-distribution performance measurement is not directly coupled with the discovery reward. This is because an iterative discovery process has a shifting reward distribution that is over-proportionally determined by the model performance for exceptional materials. We demonstrate this problem using the example of bulk modulus maximization among double perovskite oxides. We find that the in-distribution predictive performance suggests random forests as superior to Gaussian process regression, while the results are inverse in terms of the discovery rewards. We argue that the lack of proper performance estimation methods from pre-computed data collections is a fundamental problem for improving data-driven materials discovery, and we propose a novel such estimator that, in contrast to naïve reward estimation, successfully predicts Gaussian processes with the "expected improvement" acquisition function as the best out of four options in our demonstrational study for double perovskites. Importantly, it does so without requiring the over thousand ab initio computations that were needed to confirm this prediction.

cond-mat.mtrl-sci↗