Beyond KernelBoost
Roberto Rigamonti, Vincent Lepetit, Pascal Fua

TL;DR
This paper enhances the KernelBoost classifier with iterative, context-aware mechanisms and new techniques like pooling and clustering, improving biomedical image segmentation accuracy and boundary regularization.
Contribution
It introduces a novel iterative, Auto-Context inspired scheme with KernelBoost, incorporating pooling and clustering to better regularize boundaries and handle small training sets in biomedical imaging.
Findings
Outperforms state-of-the-art methods on medical datasets
Produces high-accuracy, smooth, and noise-reduced segmentations
Effectively captures pixel interactions for boundary regularization
Abstract
In this Technical Report we propose a set of improvements with respect to the KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with a scheme inspired by Auto-Context, but that is suitable in situations where the lack of large training sets poses a potential problem of overfitting. The aim is to capture the interactions between neighboring image pixels to better regularize the boundaries of segmented regions. As in Auto-Context [Tu et al., PAMI 2009] the segmentation process is iterative and, at each iteration, the segmentation results for the previous iterations are taken into account in conjunction with the image itself. However, unlike in [Tu et al., PAMI 2009], we organize our recursion so that the classifiers can progressively focus on difficult-to-classify locations. This lets us exploit the power of the decision-tree paradigm while avoiding over-fitting.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedical Image Segmentation Techniques · AI in cancer detection · Digital Imaging for Blood Diseases
