Search Results for author: Ling Zhang

Found 53 papers, 8 papers with code

Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports

no code implementations23 May 2024 Guangyu Guo, Jiawen Yao, Yingda Xia, Tony C. W. Mok, Zhilin Zheng, Junwei Han, Le Lu, Dingwen Zhang, Jian Zhou, Ling Zhang

The absence of adequately sufficient expert-level tumor annotations hinders the effectiveness of supervised learning based opportunistic cancer screening on medical imaging.

Clinical Knowledge Descriptive +2

CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios

no code implementations23 Apr 2024 Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan, Le Lu, Jiebo Luo, Ling Zhang

In this paper, we extend the scope of Med-VLP to encompass 3D images, specifically targeting full-body scenarios, by using a multimodal dataset of CT images and reports.

Contrastive Learning

CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

no code implementations7 Apr 2024 Wei Fang, Yuxing Tang, Heng Guo, Mingze Yuan, Tony C. W. Mok, Ke Yan, Jiawen Yao, Xin Chen, Zaiyi Liu, Le Lu, Ling Zhang, Minfeng Xu

In the realm of medical 3D data, such as CT and MRI images, prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution.


$M^{2}$Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction

no code implementations15 Jan 2024 Quan Liu, Jiawen Yao, Lisha Yao, Xin Chen, Jingren Zhou, Le Lu, Ling Zhang, Zaiyi Liu, Yuankai Huo

The contribution of the paper is three-fold: (1) $M^{2}$Fusion is the first pipeline of multi-level fusion on pathology WSI and 3D radiology CT image for MSI prediction; (2) CT images are the first time integrated into multimodal fusion for CRC MSI prediction; (3) feature-level fusion strategy is evaluated on both Transformer-based and CNN-based method.

Representation Learning Weakly-supervised Learning +1

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

no code implementations1 Aug 2023 Hexin Dong, Jiawen Yao, Yuxing Tang, Mingze Yuan, Yingda Xia, Jian Zhou, Hong Lu, Jingren Zhou, Bin Dong, Le Lu, Li Zhang, Zaiyi Liu, Yu Shi, Ling Zhang

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients.

Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists

no code implementations20 Jul 2023 Jianpeng Zhang, Xianghua Ye, Jianfeng Zhang, Yuxing Tang, Minfeng Xu, Jianfei Guo, Xin Chen, Zaiyi Liu, Jingren Zhou, Le Lu, Ling Zhang

In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules.

Decision Making

Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

no code implementations10 Jul 2023 Mingze Yuan, Yingda Xia, Xin Chen, Jiawen Yao, Junli Wang, Mingyan Qiu, Hexin Dong, Jingren Zhou, Bin Dong, Le Lu, Li Zhang, Zaiyi Liu, Ling Zhang

In our experiments, the proposed method achieves a sensitivity of 85. 0% and specificity of 92. 6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal.


Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings

no code implementations30 Jun 2023 Yuan Zhang, Jian Cao, Ling Zhang, Jue Chen, Wenyu Sun, YuAn Wang

The event streams generated by dynamic vision sensors (DVS) are sparse and non-uniform in the spatial domain, while still dense and redundant in the temporal domain.

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

no code implementations28 Jun 2023 Fakai Wang, Chi-Tung Cheng, Chien-Wei Peng, Ke Yan, Min Wu, Le Lu, Chien-Hung Liao, Ling Zhang

In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1, 631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks.

Lesion Detection Specificity

An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees

no code implementations3 Apr 2023 Ling Zhang, Daniel Tabas, Baosen Zhang

The challenge of finding good policies to approximate the second-stage decisions is that these solutions need to be feasible, which has been difficult to achieve with existing policies.

Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT

no code implementations2 Mar 2023 Bo Zhou, Yingda Xia, Jiawen Yao, Le Lu, Jingren Zhou, Chi Liu, James S. Duncan, Ling Zhang

Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i. e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment.

Classification Decision Making +2

An Electromagnetic-Information-Theory Based Model for Efficient Characterization of MIMO Systems in Complex Space

no code implementations13 Jan 2023 Ruifeng Li, Da Li, Jinyan Ma, Zhaoyang Feng, Ling Zhang, Shurun Tan, Wei E. I. Sha, Hongsheng Chen, Er-Ping Li

In this manuscript, an Electromagnetic-Information-Theory (EMIT) based model is developed for efficient characterization of MIMO systems in complex space.

A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging

no code implementations4 Jan 2023 Zhilin Zheng, Xu Fang, Jiawen Yao, Mengmeng Zhu, Le Lu, Lingyun Huang, Jing Xiao, Yu Shi, Hong Lu, Jianping Lu, Ling Zhang, Chengwei Shao, Yun Bian

Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors.


Document Image Shadow Removal Guided by Color-Aware Background

1 code implementation CVPR 2023 Ling Zhang, Yinghao He, Qing Zhang, Zheng Liu, Xiaolong Zhang, Chunxia Xiao

In this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document.

Decoder Document Shadow Removal +1

FedTADBench: Federated Time-Series Anomaly Detection Benchmark

1 code implementation19 Dec 2022 Fanxing Liu, Cheng Zeng, Le Zhang, Yingjie Zhou, Qing Mu, Yanru Zhang, Ling Zhang, Ce Zhu

We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning?

Anomaly Detection Federated Learning +2

SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data

no code implementations13 Jan 2022 Xingye Li, Ling Zhang, Zhigang Zhu

To reduce the reliance on labeled data, a new model called SnapshotNet is proposed as a self-supervised feature learning approach, which directly works on the unlabeled point cloud data of a complex 3D scene.

Contrastive Learning Self-Supervised Learning +2

Learning to Solve the AC Optimal Power Flow via a Lagrangian Approach

no code implementations4 Oct 2021 Ling Zhang, Baosen Zhang

Using standard and modified IEEE 22-bus, 39-bus, and 118-bus networks, we show that our approach is able to obtain the globally optimal cost even when the training data is mostly comprised of suboptimal solutions.

An Iterative Approach to Improving Solution Quality for AC Optimal Power Flow Problems

no code implementations13 Sep 2021 Ling Zhang, Baosen Zhang

In this paper, we propose a simple iterative approach to improve the quality of solutions to ACOPF problems.

CANet: A Context-Aware Network for Shadow Removal

1 code implementation ICCV 2021 Zipei Chen, Chengjiang Long, Ling Zhang, Chunxia Xiao

In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces.

Decoder Patch Matching +1

Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents

no code implementations5 Aug 2021 Ling Zhang, Jian Cao, Yuan Zhang, Bohan Zhou, Shuo Feng

This method uses distillation to effectively avoid the weakness of STBP, which can achieve SOTA performance in classification, and can obtain a smaller, faster convergence and lower power consumption SNN reinforcement learning model.

reinforcement-learning Reinforcement Learning (RL) +1

Fast PDN Impedance Prediction Using Deep Learning

no code implementations20 Jun 2021 Ling Zhang, Jack Juang, Zurab Kiguradze, Bo Pu, Shuai Jin, Songping Wu, Zhiping Yang, Chulsoon Hwang

Modeling and simulating a power distribution network (PDN) for printed circuit boards (PCBs) with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations.

Automated Discovery of Real-Time Network Camera Data From Heterogeneous Web Pages

no code implementations23 Mar 2021 Ryan Dailey, Aniesh Chawla, Andrew Liu, Sripath Mishra, Ling Zhang, Josh Majors, Yung-Hsiang Lu, George K. Thiruvathukal

Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks.

DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging

no code implementations26 Aug 2020 Jiawen Yao, Yu Shi, Le Lu, Jing Xiao, Ling Zhang

We present a multi-task CNN to accomplish both tasks of outcome and margin prediction where the network benefits from learning the tumor resection margin related features to improve survival prediction.

Survival Analysis Survival Prediction

Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation

no code implementations10 Jun 2020 Dong Yang, Holger Roth, Ziyue Xu, Fausto Milletari, Ling Zhang, Daguang Xu

For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation.

Data Augmentation Image Segmentation +5

Self-supervised Modal and View Invariant Feature Learning

no code implementations28 May 2020 Longlong Jing, Yu-cheng Chen, Ling Zhang, Mingyi He, YingLi Tian

By exploring the inherent multi-modality attributes of 3D objects, in this paper, we propose to jointly learn modal-invariant and view-invariant features from different modalities including image, point cloud, and mesh with heterogeneous networks for 3D data.

Cross-Modal Retrieval Retrieval

Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences

no code implementations13 Apr 2020 Longlong Jing, Yu-cheng Chen, Ling Zhang, Mingyi He, YingLi Tian

Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network.

3D Part Segmentation 3D Shape Classification +4

Gradient-based Feature Extraction From Raw Bayer Pattern Images

no code implementations6 Apr 2020 Wei Zhou, Ling Zhang, Shengyu Gao, Xin Lou

In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed.

Demosaicking Pedestrian Detection

Weakly supervised segmentation from extreme points

no code implementations2 Oct 2019 Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang, Daguang Xu

Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images.

BIG-bench Machine Learning Segmentation +1

Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network

1 code implementation28 Apr 2019 Ling Zhang, Zhigang Zhu

To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud "3D object" dataset by using part contrasting and object clustering with deep graph neural networks (GNNs).

Clustering Object +1

Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data

no code implementations23 Feb 2019 Ling Zhang, Le Lu, Xiaosong Wang, Robert M. Zhu, Mohammadhadi Bagheri, Ronald M. Summers, Jianhua Yao

Results validate that the ST-ConvLSTM produces a Dice score of 83. 2%+-5. 1% and a RVD of 11. 2%+-10. 8%, both significantly outperforming (p<0. 05) other compared methods of linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes.

Generative Adversarial Network Image Segmentation +3

Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction

no code implementations24 Oct 2018 Zhongyang Zhang, Ling Zhang, Ze Sun, Nicholas Erickson, Ryan From, Jun Fan

Simulating the dynamic characteristics of a PN junction at the microscopic level requires solving the Poisson's equation at every time step.

Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks

no code implementations14 Oct 2018 Haoming Lin, Yuyang Hu, Siping Chen, Jianhua Yao, Ling Zhang

However, CNN in previous studies do not involve cell morphological information, and it is unknown whether morphological features can be directly modeled by CNN to classify cervical cells.

Classification General Classification

Convolutional Invasion and Expansion Networks for Tumor Growth Prediction

no code implementations25 Jan 2018 Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao

Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics.

Self-Learning to Detect and Segment Cysts in Lung CT Images without Manual Annotation

no code implementations25 Jan 2018 Ling Zhang, Vissagan Gopalakrishnan, Le Lu, Ronald M. Summers, Joel Moss, Jianhua Yao

In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data.

Image Segmentation Lesion Detection +5

DeepPap: Deep Convolutional Networks for Cervical Cell Classification

no code implementations25 Jan 2018 Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao

However, the success of most traditional classification methods relies on the presence of accurate cell segmentations.

Classification General Classification +1

Distinguishing the Indistinguishable: Exploring Structural Ambiguities via Geodesic Context

1 code implementation CVPR 2017 Qingan Yan, Long Yang, Ling Zhang, Chunxia Xiao

A perennial problem in structure from motion (SfM) is visual ambiguity posed by repetitive structures.

Personalized Pancreatic Tumor Growth Prediction via Group Learning

no code implementations1 Jun 2017 Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao

Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor.

feature selection

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