| Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using deep learning with integrative imaging, molecular and demographic data H Duanmu, PB Huang, S Brahmavar, S Lin, T Ren, J Kong, F Wang, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 64 | 2020 |
| Convolutional neural network detection of axillary lymph node metastasis using standard clinical breast MRI T Ren, R Cattell, H Duanmu, P Huang, H Li, R Vanguri, MZ Liu, ... Clinical breast cancer 20 (3), e301-e308, 2020 | 50 | 2020 |
| Detection of suicidality among opioid users on reddit: machine learning–based approach H Yao, S Rashidian, X Dong, H Duanmu, RN Rosenthal, F Wang Journal of medical internet research 22 (11), e15293, 2020 | 47 | 2020 |
| Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients H Dammu, T Ren, TQ Duong Plos one 18 (1), e0280148, 2023 | 32 | 2023 |
| A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images H Duanmu, S Bhattarai, H Li, Z Shi, F Wang, G Teodoro, K Gogineni, ... Bioinformatics 38 (19), 4605-4612, 2022 | 23 | 2022 |
| Automatic brain organ segmentation with 3D fully convolutional neural network for radiation therapy treatment planning H Duanmu, J Kim, P Kanakaraj, A Wang, J Joshua, J Kong, F Wang 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 758-762, 2020 | 13 | 2020 |
| Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients H Duanmu, T Ren, H Li, N Mehta, AJ Singer, JM Levsky, ML Lipton, ... Biomedical engineering online 21 (1), 77, 2022 | 12 | 2022 |
| Geographic location estimation from ENF signals with high accuracy H Zhou, H Duanmu, J Li, Y Ma, J Shi, Z Tan, X Wang, L Xiang, H Yin, W Li Proc. IEEE Signal Process. Cup, 1-8, 2016 | 6 | 2016 |
| Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning S Bhattarai, G Saini, H Li, G Seth, TB Fisher, EAM Janssen, U Kiraz, ... Diagnostics 14 (1), 74, 2023 | 4 | 2023 |
| Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints H Duanmu, F Wang, G Teodoro, J Kong Bioinformatics 37 (21), 3905-3913, 2021 | 4 | 2021 |
| Spatial attention-based deep learning system for breast cancer pathological complete response prediction with serial histopathology images in multiple stains H Duanmu, S Bhattarai, H Li, CC Cheng, F Wang, G Teodoro, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 4 | 2021 |
| Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Deep Learning with Integrative Imaging, Molecular and Demographic Data H Duanmu, PB Huang, S Brahmavar, L Lin, T Ren, J Kong, F Wang, ... Proceedings of the 3rd International Conference on Medical Image Computing …, 2020 | 1 | 2020 |
| Multi-Modal, Multi-Task, and Human Knowledge Boosted Medical Image Analysis with Deep Learning H Duanmu State University of New York at Stony Brook, 2022 | | 2022 |
| Artificial Intelligence for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy from MRI and Prognostic Clinical Features H Duanmu, P Huang, S Brahmavar, F Wang, TQ Duong | | |
| Artificial Intelligence Prediction of Breast Cancer Pathologic Complete Response from Axillary Lymph Node MRIs J Yang, T Ren, H Duanmu, P Huang, R Cattell, H Li, F Wang, TQ Duong | | |
| This abstract and the presentation materials are available to members only; a login is required. H Duanmu, P Huang, S Brahmavar, F Wang, TQ Duong | | |
| Convolutional neural network classification of axillary lymph node metastasis on MRI of breast cancer patients T Ren, H Duanmu, R Cattell, R Vanguri, M Roy, MZ Liu, V Zhang, ... | | |
Fusheng WangStony Brook University在 stonybrook.edu 的电子邮件经过验证
