Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies

no code implementations • 12 Apr 2024 • Bala McRae-Posani, Andrei Holodny, Hrithwik Shalu, Joseph N Stember

Given the challenges associated with acquiring large, annotated datasets in this field, there is a need for methods that enable uncertainty quantification in small data AI approaches tailored to radiology images.

Binary Classification Uncertainty Quantification

Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification

no code implementations • 24 Dec 2021 • Joseph N Stember, Hrithwik Shalu

Purpose: Deep Neuroevolution (DNE) holds the promise of providing radiology artificial intelligence (AI) that performs well with small neural networks and small training sets.

Meta-Learning

Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets

no code implementations • 21 Oct 2020 • Joseph N Stember, Hrithwik Shalu

For comparison, we also trained and tested a keypoint detection supervised deep learning network for the same set of training / testing images.

Keypoint Detection Q-Learning +2