[2506.22504] Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2506.22504 (cs) [Submitted on 25 Jun 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection Authors:Hassan Baker, Austin J. Brockmeier View a PDF of the paper titled Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection, by Hassan Baker and 1 other authors View PDF HTML (experimental) Abstract:Detecting brain lesions as abnormalities observed in magnetic resonance imaging (MRI) is essential for diagnosis and treatment. In the search of abnormalities, such as tumors and malformations, radiologists may benefit from computer-aided diagnostics that use computer vision systems trained with machine learning to segment normal tissue from abnormal brain tissue. While supervised learning methods require annotated lesions, we propose a new unsupervised approach (Patch2Loc) that learns from normal patches taken from structural MRI. We train a neural network model to map a patch back to its spatial location within a slice of the brain volume. During inference, abnormal patches are detected by the relatively higher error and/or variance of the location prediction. This generates a heatmap that can be integrated into pixel-wise methods to achieve finer-grained segmentation. We demonstrate the ability of our model to segment abnormal brain tissues by applying our approach to the detection of tumor tissues in MRI ...