11 citations · 23 across the 8 of their papers we have counts for
9 papers · 1 filter
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
Samuel W. Remedios, Zihao Wu, Camilo Bermudez +6
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to cont…
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Yuankai Huo, Zhoubing Xu, Yunxi Xiong +7
Detailed whole brain segmentation is an essential quantitative technique, which provides a non-invasive way of measuring brain regions from a structural magnetic resonance imaging…
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Samuel Remedios, Snehashis Roy, Justin Blaber +6
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient…
Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps
Camilo Bermudez, William Rodriguez, Yuankai Huo +7
Deep brain stimulation (DBS) has the potential to improve the quality of life of people with a variety of neurological diseases. A key challenge in DBS is in the placement of a sti…
Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning
Yuankai Huo, James G. Terry, Jiachen Wang +6
Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual…
Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
Yuankai Huo, Zhoubing Xu, Shunxing Bao +8
The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to…