Portfolio

My research spans a wide range of machine learning applications in medical imaging and image analyses including image segmentation, image retrieval, quantitative bioimaging, computational neuroimaging, and computational cardiology. 

Retinal Image Analysis

End-to-end segmentation of retinal layers and fluid masses in eye OCT scans

Evidence-based
Image
Understanding

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Fast and scalable learning to hash methods  to explore large-scale heterogeneous medical databases.

Whole Body
Segmentation

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Fast, and fully automated deep learning pipeline to segment and quantify adipose tissue

Bayesian

Uncertainty

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Bayesian QuickNAT for the automated quality control of whole-brain segmentation on MRI T1 scans.

Adversarial
Attacks

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Evaluation resilience and robustness of deep learning based medical imaging networks.

Computational Cardiology

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Intravascular Ultrasound based Tissue Characterization for Assessment of Atherosclerotic Coronary Plaques. 

Computational

Pathology

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Deep Multiple Instance Convolutional Neural Networks for detection of malignancy in whole-slide histological images. 

Skin Lesion

Analysis

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Webly Supervised Learning (WSL) to train deep models for skin lesion classification

Image Reconstruction

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Complex deep networks  for reconstruction and dealiasing of undersampled MRI images