{"ID":5937906,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T07:00:23.44775862Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03740","arxiv_id":"2607.03740","title":"Triple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography","abstract":"Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive. We developed a triple-phase multimodal framework for bacterial-versus-fungal keratitis classification using slit-lamp photographs acquired under blue-light, sclerotic-scatter, and white-light illumination, together with clinical metadata. The model combines cross-modality contrastive learning, modality-specific fine-tuning, and feature-level multimodal ensemble learning for patient-level prediction. We evaluated the framework on a multicenter dataset of 1,645 patients and 17,158 images from India and the United States. The model achieved 85.84% accuracy, 84.46% average F1-score, and 0.885 AUC. Site-specific evaluation showed that pooled results were overly optimistic, whereas resampling- and balance-based re-evaluation provided a more realistic assessment of cross-site generalization. Under all settings, our framework remained the top-performing approach. Upon acceptance, the code will be released and dataset access will be provided subject to University of Michigan data-sharing clearance.","short_abstract":"Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive. We developed a triple-phase multimodal framework for bacterial-versus-fungal keratitis classification using slit-lamp photographs acquired under blue-light, sclerotic-sc...","url_abs":"https://arxiv.org/abs/2607.03740","url_pdf":"https://arxiv.org/pdf/2607.03740v1","authors":"[\"Yiqing Wang\",\"Maria A. Woodward\",\"Ziyun Yang\",\"N. Venkatesh Prajna\",\"Chunming He\",\"Leslie M. Niziol\",\"Mercy Pawar\",\"Ming-Chen Lu\",\"Guillermo Amescua\",\"Rachel Wozniak\",\"Sejal Amin\",\"Abinaya Krishnan\",\"Prabhleen Kochar\",\"Sina Farsiu\"]","published":"2026-07-04T07:06:13Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.CV\",\"eess.IV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}