1.1k citations · 1.8k across the 32 of their papers we have counts for
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Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound
Yiming Chen, Niharika S. D'Souza, Akshith Mandepally +9
Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segm…
Image-Based Soil Organic Carbon Remote Sensing from Satellite Images with Fourier Neural Operator and Structural Similarity
Ken C. L. Wong, Levente Klein, Ademir Ferreira da Silva +3
Soil organic carbon (SOC) sequestration is the transfer and storage of atmospheric carbon dioxide in soils, which plays an important role in climate change mitigation. SOC concentr…
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation
Ken C. L. Wong, Hongzhi Wang, Tanveer Syeda-Mahmood
With the introduction of Transformers, different attention-based models have been proposed for image segmentation with promising results. Although self-attention allows capturing o…
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator
Ken C. L. Wong, Hongzhi Wang, Tanveer Syeda-Mahmood
Due to the computational complexity of 3D medical image segmentation, training with downsampled images is a common remedy for out-of-memory errors in deep learning. Nevertheless, a…
Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms
Ashutosh Jadhav, Satyananda Kashyap, Hakan Bulu +6
Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could…
Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays
Neha Srivathsa, Razi Mahmood, Tanveer Syeda-Mahmood
Chest X-rays have become the focus of vigorous deep learning research in recent years due to the availability of large labeled datasets. While classification of anomalous findings…
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography
Ken C. L. Wong, Elena S. Sinkovskaya, Alfred Z. Abuhamad +1
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States. Despite training efforts and substantial advancement in…
A robust network architecture to detect normal chest X-ray radiographs
Ken C. L. Wong, Mehdi Moradi, Joy Wu +10
We propose a novel deep neural network architecture for normalcy detection in chest X-ray images. This architecture treats the problem as fine-grained binary classification in whic…
Multimodal fusion of imaging and genomics for lung cancer recurrence prediction
Vaishnavi Subramanian, Minh N. Do, Tanveer Syeda-Mahmood
Lung cancer has a high rate of recurrence in early-stage patients. Predicting the post-surgical recurrence in lung cancer patients has traditionally been approached using single mo…
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
Vaishnavi Subramanian, Hongzhi Wang, Joy T. Wu +3
Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications. They are often described in radiology repor…
Boosting the rule-out accuracy of deep disease detection using class weight modifiers
Alexandros Karargyris, Ken C. L. Wong, Joy T. Wu +2
In many screening applications, the primary goal of a radiologist or assisting artificial intelligence is to rule out certain findings. The classifiers built for such applications…