Publications

SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

A post-hoc framework that augments frozen pretrained segmentation models with a lightweight uncertainty module, modeling uncertainty as perturbation energy in a compact probe space via rank-1 posterior probes for single-forward-pass failure detection.

MIPCandy: A Modular PyTorch Framework for Medical Image Processing

A modular PyTorch framework bridging the gap between low-level component libraries and rigid monolithic pipelines for medical image processing.

ATLAS: AI-Powered Stock Market Pattern Recognition System

An AI-powered stock market pattern recognition system that applies nnU-Net's auto-configuration approach from medical image segmentation to financial time series prediction, achieving 83.4% validation accuracy with only 17,081 parameters.