168 citations · 293 across the 13 of their papers we have counts for
13 papers
MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts
Qiuhui Chen, Xinyue Hu, Zirui Wang +1
Vision-language pre-training (VLP) models have been demonstrated to be effective in many computer vision applications. In this paper, we consider developing a VLP model in the medi…
MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks
Ankita Joshi, Yi Hong
Deep learning based methods provide efficient solutions to medical image registration, including the challenging problem of diffeomorphic image registration. However, most methods…
Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs
Qiuhui Chen, Yi Hong
Structural magnetic resonance imaging (sMRI) is widely used for brain neurological disease diagnosis; while longitudinal MRIs are often collected to monitor and capture disease pro…
Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations
Qiuhui Chen, Yi Hong
Recently, weakly-supervised image segmentation using weak annotations like scribbles has gained great attention, since such annotations are much easier to obtain compared to time-c…
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network
Raunak Dey, Wenbo Sun, Haibo Xu +1
In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-le…
DDR-Net: Dividing and Downsampling Mixed Network for Diffeomorphic Image Registration
Ankita Joshi, Yi Hong
Deep diffeomorphic registration faces significant challenges for high-dimensional images, especially in terms of memory limits. Existing approaches either downsample original image…
MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation
Rutu Gandhi, Yi Hong
Segmenting an entire 3D image often has high computational complexity and requires large memory consumption; by contrast, performing volumetric segmentation in a slice-by-slice man…
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation
Raunak Dey, Yi Hong
We introduce a neural network framework, utilizing adversarial learning to partition an image into two cuts, with one cut falling into a reference distribution provided by the user…
Hybrid Cascaded Neural Network for Liver Lesion Segmentation
Raunak Dey, Yi Hong
Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning…
SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams
Nihal Soans, Ehsan Asali, Yi Hong +1
Learning from demonstration (LfD) and imitation learning offer new paradigms for transferring task behavior to robots. A class of methods that enable such online learning require t…
Predictive Image Regression for Longitudinal Studies with Missing Data
Sharmin Pathan, Yi Hong
In this paper, we propose a predictive regression model for longitudinal images with missing data based on large deformation diffeomorphic metric mapping (LDDMM) and deep neural ne…
CompNet: Complementary Segmentation Network for Brain MRI Extraction
Raunak Dey, Yi Hong
Brain extraction is a fundamental step for most brain imaging studies. In this paper, we investigate the problem of skull stripping and propose complementary segmentation networks…
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
Raunak Dey, Zhongjie Lu, Yi Hong
Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designi…