Search Results for author: Zhuotun Zhu

Found 17 papers, 1 papers with code

Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples

no code implementations29 Oct 2020 Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.

Pancreas Segmentation Semantic Segmentation +1

Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network

no code implementations29 Aug 2020 Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo, Ke Yan, Tsung-Ying Ho, Jinzheng Cai, Adam P. Harrison, Xianghua Ye, Jing Xiao, Alan Yuille, Min Sun, Le Lu, Dakai Jin

Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

no code implementations27 Aug 2020 Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung-Ying Ho, Xianghua Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, Alan Yuille, Le Lu

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

no code implementations28 Jun 2020 Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.

Pancreas Segmentation Semantic Segmentation +3

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

no code implementations27 May 2020 Zhuotun Zhu, Ke Yan, Dakai Jin, Jinzheng Cai, Tsung-Ying Ho, Adam P. Harrison, Dazhou Guo, Chun-Hung Chao, Xianghua Ye, Jing Xiao, Alan Yuille, Le Lu

We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.

Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search

no code implementations CVPR 2020 Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam P. Harrison, Chun-Hung Chao, Jing Xiao, Alan Yuille, Chien-Yu Lin, Le Lu

This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.

Neural Architecture Search

Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors

no code implementations4 Apr 2020 Zhuotun Zhu, Yongyi Lu, Wei Shen, Elliot K. Fishman, Alan L. Yuille

This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans.

Classification General Classification

V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation

no code implementations6 Jun 2019 Zhuotun Zhu, Chenxi Liu, Dong Yang, Alan Yuille, Daguang Xu

Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation.

Neural Architecture Search Semantic Segmentation +1

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

no code implementations29 Nov 2018 Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.

Medical Image Segmentation Tumor Segmentation

Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

no code implementations9 Jul 2018 Zhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans.

General Classification

Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net

no code implementations2 Apr 2018 Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman, Alan L. Yuille

There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation.

Object Recognition with and without Objects

1 code implementation20 Nov 2016 Zhuotun Zhu, Lingxi Xie, Alan L. Yuille

While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image.

Object Recognition

Bag Reference Vector for Multi-instance Learning

no code implementations3 Dec 2015 Hanqiang Song, Zhuotun Zhu, Xinggang Wang

Multi-instance learning (MIL) has a wide range of applications due to its distinctive characteristics.

Text Categorization

Relaxed Multiple-Instance SVM with Application to Object Discovery

no code implementations ICCV 2015 Xinggang Wang, Zhuotun Zhu, Cong Yao, Xiang Bai

Multiple-instance learning (MIL) has served as an important tool for a wide range of vision applications, for instance, image classification, object detection, and visual tracking.

General Classification Image Classification +4

Deep Learning Representation using Autoencoder for 3D Shape Retrieval

no code implementations25 Sep 2014 Zhuotun Zhu, Xinggang Wang, Song Bai, Cong Yao, Xiang Bai

By combing the global deep learning representation and the local descriptor representation, our method can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.

3D Shape Classification 3D Shape Recognition +3

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