DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification

Anomaly detection and classification in medical imaging are critical for early diagnosis but remain challenging due to limited annotated data, class imbalance, and the high cost of expert labeling. Emerging vision foundation models such as DINOv2, pretrained on extensive, unlabeled datasets, offer generalized representations that can potentially alleviate these limitations. In this study, we propose an attention-based global aggregation framework tailored specifically for 3D medical image anomaly classification. Leveraging the self-supervised DINOv2 model as a pretrained feature extractor, our method processes individual 2D axial slices of brain MRIs, assigning adaptive slice-level importance weights through a soft attention mechanism. To further address data scarcity, we employ a composite loss function combining supervised contrastive learning with class-variance regularization, enhancing inter-class separability and intra-class consistency. We validate our framework on the ADNI dataset and an institutional multi-class headache cohort, demonstrating strong anomaly classification performance despite limited data availability and significant class imbalance. Our results highlight the efficacy of utilizing pretrained 2D foundation models combined with attention-based slice aggregation for robust volumetric anomaly detection in medical imaging. Our implementation is publicly available at https://github.com/Rafsani/DinoAtten3D.git.

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images

Deep neural networks have revolutionized the field of supervised learning by enabling accurate predictions through learning from large annotated datasets. However, acquiring large annotated medical imaging datasets is a challenging task, especially for rare diseases, due to the high cost, time, and effort required for annotation. In these scenarios, unsupervised disease detection methods, such as anomaly detection, can save significant human effort. A typically used approach for anomaly detection is to learn the images from healthy subjects only, assuming the model will detect the images from diseased subjects as outliers. However, in many real-world scenarios, unannotated datasets with a mix of healthy and diseased individuals are available. Recent studies have shown improvement in unsupervised disease/anomaly detection using such datasets of unannotated images from healthy and diseased individuals compared to datasets that only include images from healthy individuals. A major issue remains unaddressed in these studies, which is selecting the best model for inference from a set of trained models without annotated samples. To address this issue, we propose Brainomaly, a GAN-based image-to-image translation method for neurologic disease detection using unannotated T1-weighted brain MRIs of individuals with neurologic diseases and healthy subjects. Brainomaly is trained to remove the diseased regions from the input brain MRIs and generate MRIs of corresponding healthy brains. Instead of generating the healthy images directly, Brainomaly generates an additive map where each voxel indicates the amount of changes required to make the input image look healthy. In addition, Brainomaly uses a pseudo-AUC metric for inference model selection, which further improves the detection performance. Our Brainomaly outperforms existing state-of-the-art methods by large margins.

Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.