DeepAI AI Chat
Log In Sign Up

Finding novelty with uncertainty

by   Jacob C. Reinhold, et al.
Johns Hopkins University

Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely. To address this challenge, we propose a Bayesian deep learning method that learns to translate healthy computed tomography images to magnetic resonance images and simultaneously calculates voxel-wise uncertainty. Since high uncertainty occurs in pathological regions of the image, this uncertainty can be used for unsupervised anomaly segmentation. We show encouraging experimental results on an unsupervised anomaly segmentation task by combining two types of uncertainty into a novel quantity we call scibilic uncertainty.


page 4

page 5


Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation

When applying a Deep Learning model to medical images, it is crucial to ...

Implicit field learning for unsupervised anomaly detection in medical images

We propose a novel unsupervised out-of-distribution detection method for...

ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation

Unets have become the standard method for semantic segmentation of medic...

Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment

The interpretation of medical images is a challenging task, often compli...

Plaque segmentation via masking of the artery wall

The presence of plaques in the coronary arteries are a major risk to the...

Unsupervised deep clustering for predictive texture pattern discovery in medical images

Predictive marker patterns in imaging data are a means to quantify disea...