| Magnetic resonance-based texture analysis differentiating KRAS mutation status in rectal cancer JE Oh, MJ Kim, J Lee, BY Hur, B Kim, DY Kim, JY Baek, HJ Chang, ... Cancer research and treatment: official journal of Korean Cancer Association …, 2020 | 71 | 2020 |
| Tumor identification in colorectal histology images using a convolutional neural network H Yoon, J Lee, JE Oh, HR Kim, S Lee, HJ Chang, DK Sohn Journal of digital imaging 32, 131-140, 2019 | 50 | 2019 |
| Reducing the model variance of a rectal cancer segmentation network J Lee, JE Oh, MJ Kim, BY Hur, DK Sohn IEEE Access 7, 182725-182733, 2019 | 17 | 2019 |
| Objective Detection of Oral Carcinoma with Multispectral Fluorescence Lifetime Imaging In Vivo BH Malik, J Lee, S Cheng, R Cuenca, JM Jabbour, YSL Cheng, JM Wright, ... Photochemistry and photobiology 92 (5), 694-701, 2016 | 17 | 2016 |
| Moving from 2d to 3d: volumetric medical image classification for rectal cancer staging J Lee, J Oh, I Shin, Y Kim, DK Sohn, T Kim, IS Kweon International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 4 | 2022 |
| Self-supervised pre-training improves fundus image classification for diabetic retinopathy J Lee, EJ Lee Real-Time Image Processing and Deep Learning 2022 12102, 193-198, 2022 | 3 | 2022 |
| Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attention K Lee, S Lee, S Hahn, H Hyun, E Choi, B Ahn, J Lee Machine Learning for Healthcare Conference, 423-442, 2023 | 1 | 2023 |
| Self-supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-Grained Time Resolution K Lee, J Won, H Hyun, S Hahn, E Choi, J Lee International Workshop on Trustworthy Machine Learning for Healthcare, 41-50, 2023 | | 2023 |
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