arXiv AI

Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data

The paper introduces a model‑agnostic knowledge augmentation technique that lets machine learning models leverage domain knowledge beyond automated feature extraction for weakly annotated bioimaging data. This method improves classification across seven ML architectures, achieving the best performance when combined with masked autoencoder self‑supervised learning, with precision gains of 2.9% on three in‑house microscopy datasets and 6.8% on three public cytomorphology benchmarks. Applied to nucleus‑level classification of TDP‑43 mislocalization in motor neuron images, the approach uncovers ALS‑related biological insights, such as dose‑dependent effects of stressors and variable pathology in sporadic versus familial ALS donors.

arXiv Computer Vision

Oct 28

Systematic Morphometric Brain Autoencoding for Representation Learning in Neurodegenerative Diseases

The paper introduces SYMBA, a self‑supervised pretraining framework tailored for structural T1‑weighted brain MRI. SYMBA uses anatomically constrained masking, a multi‑scale structural similarity reconstruction loss, and a progressive curriculum to capture distributed morphometric patterns. Evaluated on nine heterogeneous cohorts and two unseen datasets, SYMBA outperforms existing neuroimaging foundation models, achieving high AUROC scores for Alzheimer’s disease classification, mild cognitive impairment conversion, and amyloid status prediction, and shows strong sample efficiency in low‑label settings.

By Nuria Balb\'as-Esteban, Albert Belenguer-Llorens, Elaheh Moradi, Jussi Tohka, Vanessa G\'omez-Verdejo, Carlos Sevilla-Salcedo

arXiv Computer Vision

Oct 26

IFC-SSL-Bench: Benchmarking Self-Supervised Learning for Annotation-Efficient Multispectral Imaging Flow Cytometry

arXiv:2610.31848v1 Announce Type: new Abstract: Imaging Flow Cytometry (IFC) generates large multispectral datasets, yet the need for expert annotations bottlenecks the development of supervised deep...

By Angelo Jovin Yamachui Sitcheu, Roxana-Teodora Fetescu, Thomas Lautenschlager, Nils Friederich, Linus Schilpp, Stephan Allgeier, Luca Deininger, Daniel H\"ubschmann, Ralf Mikut

efficiency benchmarks

arXiv Computer Vision

Sep 15

AlignUS: MRI-Guided Ultrasound Representation Learning for ALS Classification from Tongue Images

arXiv:2609.15285v1 Announce Type: new Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease in which early assessment remains challenging, particularly in low-resou...

By Kadija Abdel Ghader, Emani Babe, Lorenzo Pettinari, Meya Haroune, Sidaty El Hadramy

rag efficiency multimodal

arXiv AI

Aug 11

Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

arXiv:2608. 08926v1 Announce Type: new Abstract: Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information.

By Tianli Tao, Ziyang Wang, Emma Robinson, Rachel Sparks, Le Zhang

llms rag multimodal

arXiv Computer Vision

Sep 7

A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks

The study investigates whether a compact, supervised 3D CNN pretrained for brain‑age prediction can act as a reusable foundation model for various Alzheimer's‑related neuroimaging tasks. By freezing the 7.18 million weights and adding only ~1 % of trainable parameters via Low‑Rank Adaptation, the model achieved high performance across six experiments, including dementia classification, MCI progression prediction, amyloid positivity detection, and volume estimation of hippocampal and white matter hypointensities. The results demonstrate that the pretrained brain‑age model generalizes well to new datasets without retraining, offering a data‑efficient alternative to larger networks.

By Reza Rajabli, D. Louis Collins

fine-tuning

arXiv AI

Jun 12

BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning

arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.

By Yizhou Wu, Shansong Wang, Yuheng Li, Mojtaba Safari, Mingzhe Hu, Chih-Wei Chang, Harini Veeraraghavan, Xiaofeng Yang

computer-vision fine-tuning

arXiv AI

Jul 21

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis

arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.

By Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander

computer-vision fine-tuning benchmarks

arXiv AI

Sep 15

Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...

By Paul-Gabriel Nicolae, Irina Georgiana Mocanu

llms computer-vision multimodal

arXiv Computer Vision

Oct 27

Masked and Predictive Self-Supervised Pretraining for Brain MRI: Effects of Objective Design on Disease Detection

arXiv:2606.13315v2 Announce Type: replace Abstract: MRI self-supervised learning (SSL) has largely emphasized segmentation and dense prediction, while controlled studies of how alternative pretrainin...

By Esra Erg\"un, Hersh Chandarana, Dan Sodickson, Gozde Unal

rag computer-vision fine-tuning safety

arXiv Machine Learning

Jun 24

3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy

arXiv:2606. 23964v1 Announce Type: new Abstract: Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells.

By Amirhossein Kardoost, Lion Gleiter, Tingying Peng, Carsten Marr

llms multimodal benchmarks safety

arXiv AI

Sep 28

Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation

The paper investigates combining a domain‑specific self‑supervised task—voxel‑level brain age prediction—with a general task—image inpainting—to pretrain models for brain MRI segmentation. A multitask pretraining framework jointly optimizes both objectives, yielding representations that outperform single‑task pretraining and training from scratch on three segmentation benchmarks (multiple sclerosis lesions, ischemic stroke lesions, and cortical structures). The study demonstrates that integrating domain‑specific and general self‑supervised tasks benefits the development of generalizable neuroimaging foundation models.

By Tasneem Nasser, Susanne Schmid, Roberto Souza, Naser El-Sheimy

computer-vision fine-tuning

arXiv Machine Learning

Jul 28

Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement

arXiv:2607. 22770v1 Announce Type: new Abstract: Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases.

By Siyuan Du, Mengxi Chen, Xinyang Jiang, Zilong Wang, Jiangchao Yao, Dongsheng Li, Ya Zhang, Lili Qiu, Yanfeng Wang

llms rag benchmarks safety

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