2 code implementations • 11 Apr 2024 • Jae Wan Park, Sang Hyun Park, Jun Young Koh, Junha Lee, Min Song
Finally, we mention the possibility of CAT in the aspects of multi-concept adapter and optimization.
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.
1 code implementation • CVPR 2023 • Woo Kyoung Han, Byeonghun Lee, Sang Hyun Park, Kyong Hwan Jin
Modern displays and contents support more than 8bits image and video.
1 code implementation • 15 Dec 2022 • Wei Peng, Ehsan Adeli, Tomas Bosschieter, Sang Hyun Park, Qingyu Zhao, Kilian M. Pohl
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models.
1 code implementation • 22 Jun 2022 • Philip Chikontwe, Soo Jeong Nam, Heounjeong Go, Meejeong Kim, Hyun Jung Sung, Sang Hyun Park
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 application of fully-supervised methods.
no code implementations • CVPR 2022 • 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.
no code implementations • 18 Oct 2021 • Soopil Kim, Philip Chikontwe, Sang Hyun Park
During inference, query segmentation is predicted using prototypes from both support and unlabeled images including low-level features of the query images.
no code implementations • 15 Oct 2021 • Myeongkyun Kang, Dongkyu Won, Miguel Luna, Philip Chikontwe, Kyung Soo Hong, June Hong Ahn, Sang Hyun Park
Models trained on datasets with texture bias usually perform poorly on out-of-distribution samples since biased representations are embedded into the model.
no code implementations • 18 Sep 2021 • Woohyun Kim, Pyo-Woong Son, Sul Gee Park, Sang Hyun Park, Jiwon Seo
This letter briefly summarizes the efforts of South Korea to deploy an enhanced long-range navigation (eLoran) system, which is a terrestrial low-frequency radio navigation system that can complement GNSSs.
no code implementations • 21 Apr 2021 • Heejung Park, Gyeong Min Lee, Soopil Kim, Ga Hyung Ryu, Areum Jeong, Sang Hyun Park, Min Sagong
To quickly adapt to various tasks, the meta learner was updated to get close to the center of parameters which are fine-tuned for each registration task.
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.
no code implementations • 26 Mar 2021 • Myeongkyun Kang, Philip Chikontwe, Miguel Luna, Kyung Soo Hong, June Hong Ahn, Sang Hyun Park
Our experiments show that classifiers trained with de-biased generated images report improved in-distribution performance and generalization on an external COVID-19 dataset.
no code implementations • 7 Dec 2020 • Sang Hyun Park, Shengxuan Xia, Sang-Hyun Oh, Phaedon Avouris, Tony Low
Properties of graphene plasmons are greatly affected by their coupling to phonons.
Mesoscale and Nanoscale Physics
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.
1 code implementation • 29 Sep 2020 • Philip Chikontwe, Meejeong Kim, Soo Jeong Nam, Heounjeong Go, Sang Hyun Park
To address this, recent methods have considered WSI classification as a Multiple Instance Learning (MIL) problem often with a multi-stage process for learning instance and slide level features.
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.
3 code implementations • 30 Jan 2020 • Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, Avinash Kori, Varghese Alex, Ganapathy Krishnamurthi, David Rauber, Robert Mendel, Christoph Palm, Sophia Bano, Guinther Saibro, Chi-Sheng Shih, Hsun-An Chiang, Juntang Zhuang, Junlin Yang, Vladimir Iglovikov, Anton Dobrenkii, Madhu Reddiboina, Anubhav Reddy, Xingtong Liu, Cong Gao, Mathias Unberath, Myeonghyeon Kim, Chanho Kim, Chaewon Kim, Hye-Jin Kim, Gyeongmin Lee, Ihsan Ullah, Miguel Luna, Sang Hyun Park, Mahdi Azizian, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
1 code implementation • 1 Apr 2019 • Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser, Rutger Heinen, Simon Andermatt, Mariana Bento, Matt Berseth, Mikhail Belyaev, M. Jorge Cardoso, Adrià Casamitjana, D. Louis Collins, Mahsa Dadar, Achilleas Georgiou, Mohsen Ghafoorian, Dakai Jin, April Khademi, Jesse Knight, Hongwei Li, Xavier Lladó, Miguel Luna, Qaiser Mahmood, Richard McKinley, Alireza Mehrtash, Sébastien Ourselin, Bo-yong Park, HyunJin Park, Sang Hyun Park, Simon Pezold, Elodie Puybareau, Leticia Rittner, Carole H. Sudre, Sergi Valverde, Verónica Vilaplana, Roland Wiest, Yongchao Xu, Ziyue Xu, Guodong Zeng, Jian-Guo Zhang, Guoyan Zheng, Christopher Chen, Wiesje van der Flier, Frederik Barkhof, Max A. Viergever, Geert Jan Biessels
Segmentation methods had to be containerized and submitted to the challenge organizers.
no code implementations • 15 Apr 2016 • Deepak Ghimire, Sunghwan Jeong, Joonwhoan Lee, Sang Hyun Park
But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions.