Diabetic Retinopathy Detection using Vision Transformers
Deep learning system for automated diabetic retinopathy screening achieving 94% accuracy using Vision Transformers, deployed on AWS Lambda
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portfolio
Hybrid RAG System with Graph and Vector Databases
Advanced retrieval-augmented generation system combining Neo4j knowledge graphs and Pinecone vector search, achieving 4.58/5.0 response quality
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publications
UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets
Published in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026, 2026
A novel framework for Open-Set Recognition leveraging Neural Collapse and a custom loss function to penalize open-space regions, validated across five MedMNIST datasets.
Recommended citation: Arnav Aditya, Nitin Kumar, and Saurabh Shigwan. "UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets." In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4787-4796. 2026.
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teaching
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Workshop, University 1, Department, 2015
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