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Journal of Imaging, Volume 11, Issue 9
September 2025 - 38 articles
Cover Story: Medical imaging plays a crucial role in clinical diagnosis, but deep learning models often lose accuracy when applied across different hospitals, where variations in equipment and staining create a domain shift. Test-time augmentation is one way to improve robustness by considering multiple transformed versions of each image, but it can also generate unrealistic samples that harm predictions. To overcome this, researchers developed a self-assembling strategy with out-of-distribution filtering that automatically discards unreliable samples and combines the most informative ones through weighted voting. This lightweight approach enhances cross-domain leukocyte classification, delivering more reliable predictions without additional training or models. View this paper
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J. Imaging - ISSN 2313-433X