CT

2022 | Loss odyssey in medical image segmentation

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Short Description: In this paper, we present a comprehensive review of segmentation loss functions in an organized manner. We also conduct the first large-scale analysis of 20 general loss functions on four typical 3D segmentation tasks involving six public datasets from 10+ medical centres. Our code and segmentation results are publicly available and can serve as a loss function benchmark. We hope this work will also provide insights on new loss function development for the community.
Modality: CT
Organ: Liver, Pancreas
Disease: Cancer

bottom of publications

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Privacy Preferences

When you visit our website, it may store information through your browser from specific services, usually in the form of cookies. Here you can change your Privacy preferences. It is worth noting that blocking some types of cookies may impact your experience on our website and the services we are able to offer.

Click to enable/disable Google Analytics tracking code.
Click to enable/disable Google Fonts.
Click to enable/disable Google Maps.
Click to enable/disable video embeds.
Our website uses cookies, mainly from 3rd party services. Define your Privacy Preferences and/or agree to our use of cookies.