| CBAM: Convolutional Block Attention Module S Woo, J Park, JY Lee, IS Kweon Proceedings of the European conference on computer vision (ECCV), 3-19, 2018 | 22441 | 2018 |
| BAM: Bottleneck Attention Module J Park, S Woo, JY Lee, IS Kweon Proceedings of the British Machine Vision Conference, 2018 | 1571 | 2018 |
| Reducing Domain Gap by Reducing Style Bias H Nam, HJ Lee, J Park, W Yoon, D Yoo Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 446* | 2021 |
| Distort-and-recover: Color enhancement using deep reinforcement learning J Park, JY Lee, D Yoo, IS Kweon Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 236 | 2018 |
| A simple and light-weight attention module for convolutional neural networks J Park, S Woo, JY Lee, IS Kweon International journal of computer vision 128 (4), 783-798, 2020 | 99 | 2020 |
| Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs JG Nam, M Kim, J Park, EJ Hwang, JH Lee, JH Hong, JM Goo, CM Park European Respiratory Journal 57 (5), 2021 | 84 | 2021 |
| Pt4al: Using self-supervised pretext tasks for active learning JSK Yi, M Seo, J Park, DG Choi European conference on computer vision, 596-612, 2022 | 41 | 2022 |
| Studying the effects of self-attention for medical image analysis A Rao, J Park, S Woo, JY Lee, O Aalami Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 36 | 2021 |
| Unsupervised change detection based on image reconstruction loss H Noh, J Ju, M Seo, J Park, DG Choi proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 33 | 2022 |
| Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case–control study SY Choi, S Park, M Kim, J Park, YR Choi, KN Jin Medicine 100 (16), e25663, 2021 | 21 | 2021 |
| Learning visual context by comparison M Kim, J Park, S Na, CM Park, D Yoo Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 16 | 2020 |
| A self-supervised sampler for efficient action recognition: Real-world applications in surveillance systems M Seo, D Cho, S Lee, J Park, D Kim, J Lee, J Ju, H Noh, DG Choi IEEE Robotics and Automation Letters 7 (2), 1752-1759, 2021 | 11 | 2021 |
| Apparatus for predicting metadata of medical image and method thereof J Park, D Yoo US Patent 10,824,908, 2020 | 4 | 2020 |
| Training method for specializing artificial interlligence model in institution for deployment, and apparatus for training artificial intelligence model D Yoo, P Seungwook, M Kim, J Park US Patent App. 17/689,196, 2022 | 3 | 2022 |
| Deep learning-based ensemble model using hematoxylin and eosin (H&E) whole slide images (WSIs) for the prediction of MET mutations in non-small cell lung cancer (NSCLC). YL Choi, S Park, HA Jung, JM Sun, SH Lee, JS Ahn, MJ Ahn, T Lee, ... Journal of Clinical Oncology 41 (16_suppl), e13578-e13578, 2023 | 2 | 2023 |
| Apparatus for predicting metadata of medical image and method thereof J Park, D Yoo US Patent App. 17/685,740, 2022 | 2 | 2022 |
| Apparatus and method for processing medical image using predicted metadata JC Park, DG Yoo, KH You, HS Nam, HJ Lee, SH Lee US Patent 11,978,548, 2024 | 1 | 2024 |
| Apparatus and method for processing medical image using predicted metadata JC Park, DG Yoo, KH You, HS Nam, HJ Lee, SH Lee US Patent App. 18/627,705, 2024 | | 2024 |
| Method for filtering normal medical image, method for interpreting medical image, and computing device implementing the methods J Park US Patent 11,928,817, 2024 | | 2024 |
| Method for filtering normal medical image, method for interpreting medical image, and computing device implementing the methods J Park US Patent 11,574,727, 2023 | | 2023 |
Sanghyun WooGoogle DeepMindVerified email at google.com
