Search Results for author: Jinzheng Cai

Found 24 papers, 3 papers with code

A Flexible Three-Dimensional Hetero-phase Computed Tomography Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and Practical HCC Screening

no code implementations17 Aug 2021 Chi-Tung Cheng, Jinzheng Cai, Wei Teng, Youjing Zheng, YuTing Huang, Yu-Chao Wang, Chien-Wei Peng, YouBao Tang, Wei-Chen Lee, Ta-Sen Yeh, Jing Xiao, Le Lu, Chien-Hung Liao, Adam P. Harrison

We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose.

Computed Tomography (CT)

Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection

no code implementations5 May 2021 YouBao Tang, Ke Yan, Jinzheng Cai, Lingyun Huang, Guotong Xie, Jing Xiao, JingJing Lu, Gigin Lin, Le Lu

PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.

Lesion Segmentation

Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss

no code implementations3 May 2021 YouBao Tang, Jinzheng Cai, Ke Yan, Lingyun Huang, Guotong Xie, Jing Xiao, JingJing Lu, Gigin Lin, Le Lu

Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS).

Computed Tomography (CT) Lesion Segmentation +1

Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies

1 code implementation CVPR 2021 Jinzheng Cai, YouBao Tang, Ke Yan, Adam P. Harrison, Jing Xiao, Gigin Lin, Le Lu

In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.

3D Object Tracking

Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT

1 code implementation5 Sep 2020 Ke Yan, Jinzheng Cai, Youjing Zheng, Adam P. Harrison, Dakai Jin, YouBao Tang, Yuxing Tang, Lingyun Huang, Jing Xiao, Le Lu

For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).

Transfer Learning

Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression

no code implementations30 Aug 2020 Jinzheng Cai, Ke Yan, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu, Adam P. Harrison

Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians.

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.

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 Unsupervised Domain Adaptation +1

Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets

no code implementations28 May 2020 Ke Yan, Jinzheng Cai, Adam P. Harrison, Dakai Jin, Jing Xiao, Le Lu

First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.

Transfer Learning

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.

Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation

no code implementations ECCV 2020 Ashwin Raju, Chi-Tung Cheng, Yunakai Huo, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHuang Liao, Adam P. Harrison

In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments.

Computed Tomography (CT) Domain Adaptation +1

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

1 code implementation21 Jan 2020 Jinzheng Cai, Adam P. Harrison, Youjing Zheng, Ke Yan, Yuankai Huo, Jing Xiao, Lin Yang, Le Lu

This is the goal of our work, where we develop a powerful system to harvest missing lesions from the DeepLesion dataset at high precision.

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

no code implementations15 Oct 2019 Jinzheng Cai, Yingda Xia, Dong Yang, Daguang Xu, Lin Yang, Holger Roth

However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs.

Pancreas Segmentation

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

CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement

no code implementations18 Jul 2018 Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.

Computed Tomography (CT) Image Enhancement +2

Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

no code implementations3 Jul 2018 Jinzheng Cai, Le Lu, Adam P. Harrison, Xiaoshuang Shi, Pingjun Chen, Lin Yang

Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images.

General Classification Image Classification

Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning

no code implementations30 Mar 2018 Jinzheng Cai, Le Lu, Fuyong Xing, Lin Yang

Automatic pancreas segmentation in radiology images, eg., computed tomography (CT) and magnetic resonance imaging (MRI), is frequently required by computer-aided screening, diagnosis, and quantitative assessment.

Computed Tomography (CT) Pancreas Segmentation

Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

no code implementations25 Jan 2018 Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.

Lesion Segmentation Super-Resolution

Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function

no code implementations16 Jul 2017 Jinzheng Cai, Le Lu, Yuanpu Xie, Fuyong Xing, Lin Yang

The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner.

Pancreas Segmentation

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