Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection

no code implementations • 21 Dec 2023 • Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, Sang Hyun Park

In this study, we introduce a novel component segmentation model for LA detection that leverages a few labeled samples and unlabeled images sharing logical constraints.

Anomaly Detection Segmentation +1

Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs

no code implementations • 6 Apr 2021 • Dongkyu Won, Euijin Jung, Sion An, Philip Chikontwe, Sang Hyun Park

The proposed ensemble noise model can generate realistic CT noise, and thus our method significantly improves the denoising performance existing denoising models trained by supervised- and self-supervised learning.

Image Denoising Self-Supervised Learning

Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation

no code implementations • 19 Nov 2020 • Soopil Kim, Sion An, Philip Chikontwe, Sang Hyun Park

In this paper, we propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation.

Few-Shot Learning Image Segmentation +5

Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

no code implementations • 3 Mar 2020 • Sion An, Soopil Kim, Philip Chikontwe, Sang Hyun Park

In addition to the unified learning of feature similarity and a few shot classifier, our method leads to emphasize informative features in support data relevant to the query data, which generalizes better on unseen subjects.

Autonomous Driving Classification +4