ArticleAnnals of medicine and surgery (2012)2025
SAAM-VetNet: an attention-based multi-task framework for animal disease detection and severity grading.
Article in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A severity-aware multi-task CNN-transformer framework for explainable diabetic foot ulcer triage and mobile deployment.Frontiers in artificial intelligence · 2026Article
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Authors and funding
9 authors.
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Abstract
Early and accurate detection of animal diseases is critical in veterinary medicine and preclinical research, where timely intervention can influence both animal welfare and experimental outcomes. In this study, we introduce SAAM-VetNet, a novel Severity-Aware Attention-Based Multi-Task deep learning framework designed to simultaneously detect animal diseases and grade their severity from medical images. The proposed architecture integrates a convolutional block attention module to enhance feature localization and contextual representation, coupled with a multi-branch learning strategy for disease classification and severity assessment. We evaluate SAAM-VetNet using two publicly available datasets: the Animal Disease Classification dataset and the Mastitis Disease Detection dataset. Our model achieves superior performance with an accuracy of 91.2% and an
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Registered trials
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