arXiv AI August 11, 2026 By Saed Moradi, Benyamin Ghojogh, M. Hadi Sepanj, Yimin Yang, Ashirbani Saha

LoRSA: Toward Generalizable Parameter-Efficient Fine-Tuning for Biomedical Downstream Tasks

arXiv:2608. 07749v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning enables the adaptation of vision foundation models to biomedical tasks under limited computational resources, but a single low-rank update can constrain all task-specific changes to one narrow parameter subspace.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

fine-tuning

arXiv AI

Jun 8

DaX: Learning General Pathology Representations Across Scales

arXiv:2606. 06983v1 Announce Type: cross Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution.

By Bokai Zhao, Yiyang Zhang, Long Bai, Tai Ma, Hanqing Chao, Minfeng Xu

benchmarks

arXiv AI

Jul 14

Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift

arXiv:2607. 10358v1 Announce Type: cross Abstract: Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains unclear.

By Giang Nguyen, Raghav Mehta, Emma A. M. Stanley, Tian Xia, Thi Hao Nguyen, Hieu Pham, Ben Glocker

llms multimodal benchmarks

arXiv AI

Jul 17

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening

arXiv:2607. 15047v1 Announce Type: cross Abstract: Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries.

By Javad Khoramdel, Farhad Hoseyni, Amirhossein Nikoofard

rag fine-tuning

arXiv AI

Jun 9

Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis

arXiv:2606. 08364v1 Announce Type: cross Abstract: Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative condition whose osseous changes are often subtle on cone-beam CT (CBCT), making automated detection challenging.

By Shradhdha Trivedi, Vrundan Sojitra, Mariela Padilla

llms computer-vision benchmarks

arXiv AI

6d ago

COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification

arXiv:2505. 18315v3 Announce Type: replace-cross Abstract: We introduce \textbf{CoLoRA} (Convolutional Low-Rank Adaptation), a parameter-efficient fine-tuning method for convolutional neural networks (CNNs).

By Mariano Rivera, Angello Hoyos

computer-vision fine-tuning

arXiv AI

Jul 8

Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification

arXiv:2607. 06309v1 Announce Type: cross Abstract: Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities.

By Aysan Ghayouri Pirsoltan, Shima Babakordi, Mohammad Reza Mohammadi

llms computer-vision benchmarks

Related stories

DaX: Learning General Pathology Representations Across Scales

Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening

Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis

COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification

Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification