Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection

Hassan Baker, Austin J. Brockmeier
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:3727-3735, 2026.

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 anomaly score based on the error and variance of the location prediction. By applying the network in a convolutional manner, this generates a pixel-wise heatmap of anomalies providing 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 on T2-weighted images from BraTS2021 and MSLUB datasets and T1-weighted images from ATLAS and WMH datasets. We show that it outperforms the state-of-the art in unsupervised segmentation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-baker26a, title = { Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection }, author = {Baker, Hassan and Brockmeier, Austin J.}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {3727--3735}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/baker26a/baker26a.pdf}, url = {https://proceedings.mlr.press/v300/baker26a.html}, 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 anomaly score based on the error and variance of the location prediction. By applying the network in a convolutional manner, this generates a pixel-wise heatmap of anomalies providing 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 on T2-weighted images from BraTS2021 and MSLUB datasets and T1-weighted images from ATLAS and WMH datasets. We show that it outperforms the state-of-the art in unsupervised segmentation. } }
Endnote
%0 Conference Paper %T Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection %A Hassan Baker %A Austin J. Brockmeier %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-baker26a %I PMLR %P 3727--3735 %U https://proceedings.mlr.press/v300/baker26a.html %V 300 %X 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 anomaly score based on the error and variance of the location prediction. By applying the network in a convolutional manner, this generates a pixel-wise heatmap of anomalies providing 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 on T2-weighted images from BraTS2021 and MSLUB datasets and T1-weighted images from ATLAS and WMH datasets. We show that it outperforms the state-of-the art in unsupervised segmentation.
APA
Baker, H. & Brockmeier, A.J.. (2026). Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:3727-3735 Available from https://proceedings.mlr.press/v300/baker26a.html.

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