46 citations · 66 across the 11 of their papers we have counts for
11 papers
FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration
Kuan-Yen Chen, Fang-Yi Su, Philip Chikontwe +1
Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference. However, existing lossy comp…
Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
Qian Da, Yijiang Chen, Min Ju +25
Recent breakthroughs in artificial intelligence through foundation models and agents have accelerated the evolution of computational pathology. Demonstrated performance gains repor…
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification
Sion An, Myeongkyun Kang, Soopil Kim +3
Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variab…
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…
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs
Dongkyu Won, Euijin Jung, Sion An +2
Recently, Self-supervised learning methods able to perform image denoising without ground truth labels have been proposed. These methods create low-quality images by adding random…
Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing
Myeongkyun Kang, Philip Chikontwe, Miguel Luna +3
Following the pandemic outbreak, several works have proposed to diagnose COVID-19 with deep learning in computed tomography (CT); reporting performance on-par with experts. However…
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…
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification
Sion An, Soopil Kim, Philip Chikontwe +1
Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) signals, in particular motor imagery (MI) data have received a lot of attention and show the potential towards…