| Measuring the effects of non-identical data distribution for federated visual classification TMH Hsu, H Qi, M Brown arXiv preprint arXiv:1909.06335, 2019 | 1070 | 2019 |
| Clinically Accurate Chest X-Ray Report Generation G Liu, TMH Hsu, M McDermott, W Boag, WH Weng, P Szolovits, ... Machine Learning for Healthcare Conference, 249-269, 2019 | 280 | 2019 |
| 3d-aware scene manipulation via inverse graphics S Yao, TM Hsu, JY Zhu, J Wu, A Torralba, B Freeman, J Tenenbaum Advances in neural information processing systems 31, 2018 | 274 | 2018 |
| Federated visual classification with real-world data distribution TMH Hsu, H Qi, M Brown Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 185 | 2020 |
| Unsupervised domain adaptation with imbalanced cross-domain data T Ming Harry Hsu, W Yu Chen, CA Hou, YH Hubert Tsai, YR Yeh, ... Proceedings of the IEEE International Conference on Computer Vision, 4121-4129, 2015 | 91 | 2015 |
| Learning food quality and safety from wireless stickers U Ha, Y Ma, Z Zhong, TM Hsu, F Adib Proceedings of the 17th ACM workshop on hot topics in networks, 106-112, 2018 | 80 | 2018 |
| Transfer Neural Trees for Heterogeneous Domain Adaptation WY Chen, TMH Hsu, YHH Tsai, YCF Wang, MS Chen Computer Vision (ECCV), 2016 European Conference on, 2016 | 76 | 2016 |
| Baselines for chest x-ray report generation W Boag, TMH Hsu, M McDermott, G Berner, E Alesentzer, P Szolovits Machine learning for health workshop, 126-140, 2020 | 62 | 2020 |
| Unsupervised Multimodal Representation Learning across Medical Images and Reports TMH Hsu, WH Weng, W Boag, M McDermott, P Szolovits Machine Learning for Health (ML4H) Workshop at NeurIPS 2018, 2018 | 46 | 2018 |
| Artificial intelligence to assess body composition on routine abdominal CT scans and predict mortality in pancreatic cancer–A recipe for your local application TMH Hsu, K Schawkat, SJ Berkowitz, JL Wei, A Makoyeva, K Legare, ... European journal of radiology 142, 109834, 2021 | 36 | 2021 |
| Chexpert++: Approximating the chexpert labeler for speed, differentiability, and probabilistic output MBA McDermott, TMH Hsu, WH Weng, M Ghassemi, P Szolovits Machine Learning for Healthcare Conference, 913-927, 2020 | 35 | 2020 |
| Visceral adiposity and severe COVID-19 disease: application of an artificial intelligence algorithm to improve clinical risk prediction A Goehler, TMH Hsu, JA Seiglie, MJ Siedner, J Lo, V Triant, J Hsu, ... Open forum infectious diseases 8 (7), ofab275, 2021 | 22 | 2021 |
| Three-dimensional neural network to automatically assess liver tumor burden change on consecutive liver MRIs A Goehler, TMH Hsu, R Lacson, I Gujrathi, R Hashemi, G Chlebus, ... Journal of the American College of Radiology 17 (11), 1475-1484, 2020 | 18 | 2020 |
| Transfer neural trees: Semi-supervised heterogeneous domain adaptation and beyond WY Chen, TMH Hsu, YHH Tsai, MS Chen, YCF Wang IEEE Transactions on Image Processing 28 (9), 4620-4633, 2019 | 18 | 2019 |
| Positional assessment of lower third molar and mandibular canal using explainable artificial intelligence S Kempers, P van Lierop, TMH Hsu, DA Moin, S Bergé, H Ghaeminia, T Xi, ... Journal of Dentistry 133, 104519, 2023 | 10 | 2023 |
| Adversarial contrastive pre-training for protein sequences M McDermott, B Yap, H Hsu, D Jin, P Szolovits arXiv preprint arXiv:2102.00466, 2021 | 10 | 2021 |
| Emulating clinical diagnostic reasoning for jaw cysts with machine learning B Feher, U Kuchler, F Schwendicke, L Schneider, ... Diagnostics 12 (8), 1968, 2022 | 9 | 2022 |
| Intra-oral scan segmentation using deep learning S Vinayahalingam, S Kempers, J Schoep, TMH Hsu, DA Moin, ... BMC Oral Health 23 (1), 643, 2023 | 8 | 2023 |
| DeepOPG: Improving Orthopantomogram Finding Summarization with Weak Supervision TMH Hsu, YCC Wang Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 4 | 2021 |
| Automatic longitudinal assessment of tumor responses TMH Hsu Massachusetts Institute of Technology, 2020 | 2 | 2020 |
Peter SzolovitsProfessor of Computer Science and Engineering, MIT在 mit.edu 的電子郵件地址已通過驗證
