| Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging N Arun, N Gaw, P Singh, K Chang, M Aggarwal, B Chen, K Hoebel, ... Radiology: Artificial Intelligence 3 (6), e200267, 2021 | 200 | 2021 |
| Federated learning for breast density classification: A real-world implementation HR Roth, K Chang, P Singh, N Neumark, W Li, V Gupta, S Gupta, L Qu, ... Domain Adaptation and Representation Transfer, and Distributed and …, 2020 | 175 | 2020 |
| Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging MD Li, K Chang, B Bearce, CY Chang, AJ Huang, JP Campbell, ... NPJ digital medicine 3 (1), 48, 2020 | 97 | 2020 |
| A disintegrin and metalloprotease 17 dynamic interaction sequence, the sweet tooth for the human interleukin 6 receptor S Düsterhöft, K Höbel, M Oldefest, J Lokau, GH Waetzig, A Chalaris, ... Journal of Biological Chemistry 289 (23), 16336-16348, 2014 | 89 | 2014 |
| DeepNeuro: an open-source deep learning toolbox for neuroimaging A Beers, J Brown, K Chang, K Hoebel, J Patel, KI Ly, SM Tolaney, ... Neuroinformatics 19, 127-140, 2021 | 51 | 2021 |
| Machine learning models can detect aneurysm rupture and identify clinical features associated with rupture MA Silva, J Patel, V Kavouridis, T Gallerani, A Beers, K Chang, KV Hoebel, ... World Neurosurgery 131, e46-e51, 2019 | 47 | 2019 |
| Multi-institutional assessment and crowdsourcing evaluation of deep learning for automated classification of breast density K Chang, AL Beers, L Brink, JB Patel, P Singh, NT Arun, KV Hoebel, ... Journal of the American College of Radiology 17 (12), 1653-1662, 2020 | 43 | 2020 |
| Radiomics repeatability pitfalls in a scan-rescan MRI study of glioblastoma KV Hoebel, JB Patel, AL Beers, K Chang, P Singh, JM Brown, MC Pinho, ... Radiology: Artificial Intelligence 3 (1), e190199, 2020 | 41 | 2020 |
| An exploration of uncertainty information for segmentation quality assessment K Hoebel, V Andrearczyk, A Beers, J Patel, K Chang, A Depeursinge, ... Medical Imaging 2020: Image Processing 11313, 381-390, 2020 | 39 | 2020 |
| QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... The journal of machine learning for biomedical imaging 2022, 2022 | 32 | 2022 |
| Fair conformal predictors for applications in medical imaging C Lu, A Lemay, K Chang, K Höbel, J Kalpathy-Cramer Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 12008 …, 2022 | 32 | 2022 |
| Assessing the validity of saliency maps for abnormality localization in medical imaging NT Arun, N Gaw, P Singh, K Chang, KV Hoebel, J Patel, M Gidwani, ... arXiv preprint arXiv:2006.00063, 2020 | 23 | 2020 |
| Improving the repeatability of deep learning models with Monte Carlo dropout A Lemay, K Hoebel, CP Bridge, B Befano, S De Sanjosé, D Egemen, ... npj Digital Medicine 5 (1), 174, 2022 | 20 | 2022 |
| Inconsistent partitioning and unproductive feature associations yield idealized radiomic models M Gidwani, K Chang, JB Patel, KV Hoebel, SR Ahmed, P Singh, CD Fuller, ... Radiology 307 (1), e220715, 2022 | 17 | 2022 |
| Balloon catheter-based radiofrequency ablation monitoring in porcine esophagus using optical coherence tomography WCY Lo, N Uribe-Patarroyo, K Hoebel, K Beaudette, M Villiger, ... Biomedical Optics Express 10 (4), 2067-2089, 2019 | 17 | 2019 |
| Addressing catastrophic forgetting for medical domain expansion S Gupta, P Singh, K Chang, L Qu, M Aggarwal, N Arun, A Vaswani, ... arXiv preprint arXiv:2103.13511, 2021 | 15 | 2021 |
| Segmentation, survival prediction, and uncertainty estimation of gliomas from multimodal 3D MRI using selective kernel networks J Patel, K Chang, K Hoebel, M Gidwani, N Arun, S Gupta, M Aggarwal, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2021 | 11 | 2021 |
| Focal loss improves repeatability of deep learning models SR Ahmed, A Lemay, KV Hoebel, J Kalpathy-Cramer Medical Imaging with Deep Learning, 2022 | 8 | 2022 |
| Evaluating subgroup disparity using epistemic uncertainty in mammography C Lu, A Lemay, K Hoebel, J Kalpathy-Cramer arXiv preprint arXiv:2107.02716, 2021 | 8 | 2021 |
| FDU-net: deep learning-based three-dimensional diffuse optical image reconstruction B Deng, H Gu, H Zhu, K Chang, KV Hoebel, JB Patel, J Kalpathy-Cramer, ... IEEE Transactions on Medical Imaging, 2023 | 5 | 2023 |
