46 citations · 66 across the 11 of their papers we have counts for
5 papers · 1 filter
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Soopil Kim, Sion An, Philip Chikontwe +4
Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately su…
Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification
Philip Chikontwe, Soo Jeong Nam, Heounjeong Go +3
Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases; but, curation of accurate labels is time-consuming and limits the applicat…
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification
Philip Chikontwe, Soopil Kim, Sang Hyun Park
Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extr…
Uncertainty-Aware Semi-Supervised Few Shot Segmentation
Soopil Kim, Philip Chikontwe, Sang Hyun Park
Few shot segmentation (FSS) aims to learn pixel-level classification of a target object in a query image using only a few annotated support samples. This is challenging as it requi…
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation
Soopil Kim, Sion An, Philip Chikontwe +1
Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success…