[HTML][HTML] A review of uncertainty estimation and its application in medical imaging
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …
importance. Deep learning has shown great promise in medical imaging, but the reliability …
A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
A bayesian neural net to segment images with uncertainty estimates and good calibration
We propose a novel Bayesian decision theoretic deep-neural-network (DNN) framework for
image segmentation, enabling us to define a principled measure of uncertainty associated …
image segmentation, enabling us to define a principled measure of uncertainty associated …
Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification
Accurate and automated cardiac bi-ventricle quantification based on cardiac magnetic
resonance (CMR) image is a very crucial procedure for clinical cardiac disease diagnosis …
resonance (CMR) image is a very crucial procedure for clinical cardiac disease diagnosis …
Cq-vae: Coordinate quantized vae for uncertainty estimation with application to disk shape analysis from lumbar spine mri images
Ambiguity is inevitable in medical images, which often results in different image
interpretations (eg object boundaries or segmentation maps) from different human experts …
interpretations (eg object boundaries or segmentation maps) from different human experts …