Medical AI
Deep learning for digital pathology, radiology, and clinical decision support.
- • Lupus Nephritis Classification
- • Glioma Diagnosis
- • Breast Cancer Subtyping
- • Multiple Instance Learning
Research
I work at the intersection of machine learning and medicine, building deep-learning systems for medical image analysis, computer vision, and applied AI. My published work spans gigapixel histopathology, multiple-instance learning, and curated medical datasets. Selected publications and research interests are listed below.
Amit Sharma et al.
A deep-learning pipeline for classifying lupus nephritis subtypes directly from gigapixel digital whole-slide images, designed to operate efficiently in resource-constrained clinical environments.
Amit Sharma et al.
A curated brain-tumor dataset benchmarking multiple-instance learning methods for glioma diagnosis, achieving a 12% improvement over prior state-of-the-art on 3-way classification.
Amit Sharma et al.
Benchmarks of MIL approaches on H&E whole-slide images for diffuse glioma classification, with extensive ablations on feature extractors and aggregation strategies.
Deep learning for digital pathology, radiology, and clinical decision support.
Core methods that power my applied work — from representation learning to multimodal fusion.
Bridging research and product, with a focus on usable, deployable AI systems.
I'm open to research collaborations and applied ML projects. Take a look at the portfolio or hire me for engineering work, or read more on the blog.