1.1k citations · 1.8k across the 32 of their papers we have counts for
34 papers
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…
Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects
Elisa Warner, Joonsang Lee, William Hsu +4
Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning…
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…
MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini +4
With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data…
Medical visual question answering using joint self-supervised learning
Yuan Zhou, Jing Mei, Yiqin Yu +1
Visual Question Answering (VQA) becomes one of the most active research problems in the medical imaging domain. A well-known VQA challenge is the intrinsic diversity between the im…
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Rodrigo Bonazzola, Enzo Ferrante, Nishant Ravikumar +5
Recent genome-wide association studies (GWAS) have been successful in identifying associations between genetic variants and simple cardiac parameters derived from cardiac magnetic…
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…
Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini +4
In a complex disease such as tuberculosis, the evidence for the disease and its evolution may be present in multiple modalities such as clinical, genomic, or imaging data. Effectiv…
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…
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Ken C. L. Wong, Hongzhi Wang, Etienne E. Vos +3
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows…
Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics
Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do
The availability of multi-modality datasets provides a unique opportunity to characterize the same object of interest using multiple viewpoints more comprehensively. In this work,…
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…
Multimodal fusion using sparse CCA for breast cancer survival prediction
Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do
Effective understanding of a disease such as cancer requires fusing multiple sources of information captured across physical scales by multimodal data. In this work, we propose a n…
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Tanveer Syeda-Mahmood, Ph. D, K. C. L Wong +6
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray d…
Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA
Karina Kanjaria, Anup Pillai, Chaitanya Shivade +4
Due to advances in machine learning and artificial intelligence (AI), a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows. But…
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Satyananda Kashyap, Alexandros Karargyris, Joy Wu +5
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses…
Chest X-ray Report Generation through Fine-Grained Label Learning
Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur +9
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the…
Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation
John Paul Francis, Hongzhi Wang, Kate White +2
The Pancreatic beta cell is an important target in diabetes research. For scalable modeling of beta cell ultrastructure, we investigate automatic segmentation of whole cell imaging…
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…