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
arXiv:2607. 21068v1 Announce Type: new Abstract: Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized.
By Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya Chatterjee
arXiv:2607. 08254v1 Announce Type: new Abstract: Quantifying variability in a target population relative to a reference population is central to many scientific and clinical problems (e.
By Sai Spandana Chintapalli, Pratik Chaudhari, Christos Davatzikos
The study introduces an explainable OCTA pipeline for Alzheimer’s disease phenotyping that combines vessel segmentation, layer‑specific biomarker extraction, and label‑free phenotyping with large language model (LLM) reporting. Using 117 images from 39 subjects, the segmentation models achieved high ROC‑AUC (0.916–0.970) and Dice scores (0.695–0.781), and six vascular biomarkers were used to create subject‑level profiles for exploratory clustering. LLMs (GPT, Gemini, Llama) produced measurement‑grounded reports evaluated for citation faithfulness and diagnostic caution, offering a transparent, non‑diagnostic link between retinal vascular data and Alzheimer’s research.
By Progga Paromita Dutta, Jeba Maliha, Md Rafiul Kabir
arXiv:2608. 09752v1 Announce Type: cross Abstract: Retinal fundus images frequently exhibit multiple co-occurring pathologies, yet standard deep learning classifiers apply static, identical computation to every image regardless of the underlying disease distribution.
By Nagur Shareef Shaik, Jeongwoo Park, Yeong-Jin Kim, Jaeuk Jung, Hyunjung Oh, Dong Hye Ye
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