arXiv:2607. 04673v1 Announce Type: cross Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer little clinical interpretability.
By Cheng Huang, Jia Zhang, Yi Jiang, Yang Liu, Karanjit Kooner, Yadi Liu, Tsengdar Lee, Yang Xie, Wenqi Shi, Guanghua Xiao
arXiv:2609.06699v1 Announce Type: cross
Abstract: Glaucoma is a progressive optic neuropathy characterized by irreversible damage to the optic nerve, making timely diagnosis critical to prevent perma...
By Abdullah Al Shafi, Nishat Sadaf Lira, Abrar Hasan, Kazi Saeed Alam, Swapnil Kundu Argha
arXiv:2607. 25324v1 Announce Type: cross Abstract: Glaucoma is a group of eye diseases that damage the optic nerve, often caused by elevated intraocular pressure.
By Sai Venkatesh Chilukoti, Krishna Rauniyar, Min Shi, Xiali Hei
OptiModNet is a lightweight UNet‑Transformer hybrid designed for optic disc and cup segmentation. It incorporates grouped‑query and channel attention across multiple stages, along with an Aggregated Pyramid Loss to improve gradient flow and structural consistency. Evaluated on the REFUGE2 dataset, it surpasses existing methods by over 2.5 % while using only 3.73 GFLOPs and 1.93 M parameters.
By Soumili Ghosh, Debapriya Roy, Aryan Das, Bikash Santra
Deep learning framework GLAM predicts glaucoma progression rates from longitudinal Humphrey 24‑2 visual field data and five clinical features, achieving a mean absolute error of 0.139 dB yr⁻¹ and an AUC of 0.990 for fast‑progressor detection. Using attention‑based fusion and aleatoric uncertainty, GLAM outperforms a ridge regression baseline by 73.5% in MD‑rate prediction. The model demonstrates that visual field data alone can match multimodal pipelines for progression prognostication.
By Taiabur Rahman, Siddiqur Rahman, Muhammad Moniruzzaman, Ummay Kawsar, Sayedatunnessa Ratna, Shadman Siddique, Rafsan Siddique, Tausif Ahmad, Tahsin Ahmad, Golam Rabbani
arXiv:2608. 10316v1 Announce Type: cross Abstract: Multi-modal learning combining medical images and clinical text is promising for disease diagnosis.
By Zijian Gu, Weikai Lin, Shuang Zhou, Zihan Chen, Song Wang