239 citations · 513 across the 18 of their papers we have counts for
7 papers · 1 filter
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Jie Yang, Siqi Liu, Sasa Grbic +7
Though large-scale datasets are essential for training deep learning systems, it is expensive to scale up the collection of medical imaging datasets. Synthesizing the objects of in…
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
Siqi Liu, Eli Gibson, Sasa Grbic +5
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations. Such systems require data to be annotated by experts with years…
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…
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon +6
A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically requir…
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Yuankai Huo, Zhoubing Xu, Katherine Aboud +6
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy. Historically, multi-atlas segmentation (MAS)…
Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images
Zhoubing Xu, Yuankai Huo, JinHyeong Park +4
An abdominal ultrasound examination, which is the most common ultrasound examination, requires substantial manual efforts to acquire standard abdominal organ views, annotate the vi…
Select, Attend, and Transfer: Light, Learnable Skip Connections
Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma +8
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risk…