Search Results for author: Yongbing Zhang

Found 30 papers, 15 papers with code

H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images

no code implementations27 Mar 2024 Jinpeng Lu, Jingyun Chen, Linghan Cai, Songhan Jiang, Yongbing Zhang

However, modality-specific encoders used in these methods operate independently, inadequately leveraging the synergistic relationships inherent in PET and CT modalities, for example, the complementarity between semantics and structure.

Computed Tomography (CT) Tumor Segmentation

MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models

no code implementations8 Mar 2024 Zijie Fang, Yifeng Wang, Zhi Wang, Jian Zhang, Xiangyang Ji, Yongbing Zhang

To tackle this challenge, we propose a MamMIL framework for WSI classification by cooperating the selective structured state space model (i. e., Mamba) with MIL for the first time, enabling the modeling of instance dependencies while maintaining linear complexity.

Multiple Instance Learning whole slide images

SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation

1 code implementation18 Jan 2024 Ye Zhang, Linghan Cai, Ziyue Wang, Yongbing Zhang

Concretely, SEINE introduces a contour-based structure encoding (SE) that considers the correlation between nuclei structure and semantics, realizing a reasonable representation of the nuclei structure.

Instance Segmentation Semantic Segmentation

A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

1 code implementation11 Sep 2023 Linghan Cai, Jianhao Huang, Zihang Zhu, Jinpeng Lu, Yongbing Zhang

However, precise tumor segmentation is challenging due to the small size of many tumors and the similarity of high-uptake normal areas to the tumor regions.

Computed Tomography (CT) Lesion Segmentation +2

Self-Supervised Scalable Deep Compressed Sensing

1 code implementation26 Aug 2023 Bin Chen, Xuanyu Zhang, Shuai Liu, Yongbing Zhang, Jian Zhang

Compressed sensing (CS) is a promising tool for reducing sampling costs.

HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image

1 code implementation30 Jun 2023 Zhuchen Shao, Yang Chen, Hao Bian, Jian Zhang, Guojun Liu, Yongbing Zhang

Many studies adopt random sampling pre-processing strategy and WSI-level aggregation models, which inevitably lose critical prognostic information in the patient-level bag.

Multiple Instance Learning Survival Prediction +1

Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

1 code implementation5 May 2023 Chuang Zhu, ShengJie Liu, Zekuan Yu, Feng Xu, Arpit Aggarwal, Germán Corredor, Anant Madabhushi, Qixun Qu, Hongwei Fan, Fangda Li, Yueheng Li, Xianchao Guan, Yongbing Zhang, Vivek Kumar Singh, Farhan Akram, Md. Mostafa Kamal Sarker, Zhongyue Shi, Mulan Jin

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan.

Image Generation SSIM

Progressive Content-aware Coded Hyperspectral Compressive Imaging

no code implementations17 Mar 2023 Xuanyu Zhang, Bin Chen, Wenzhen Zou, Shuai Liu, Yongbing Zhang, Ruiqin Xiong, Jian Zhang

Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology.

AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image

no code implementations11 Mar 2023 Zhuchen Shao, Liuxi Dai, Yifeng Wang, Haoqian Wang, Yongbing Zhang

Moreover, we highlight AugDiff's higher-quality augmented feature over image augmentation and its superiority over self-supervised learning.

Image Augmentation Multiple Instance Learning +3

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

1 code implementation11 Mar 2023 Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.

Nuclear Segmentation Segmentation +2

Cross Aggregation Transformer for Image Restoration

3 code implementations24 Nov 2022 Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan

The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows.

Image Restoration Inductive Bias

Accurate Image Restoration with Attention Retractable Transformer

1 code implementation4 Oct 2022 Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan

This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions.

Denoising Image Restoration +2

Multiple Instance Learning with Mixed Supervision in Gleason Grading

1 code implementation26 Jun 2022 Hao Bian, Zhuchen Shao, Yang Chen, Yifeng Wang, Haoqian Wang, Jian Zhang, Yongbing Zhang

We achieve the state-of-the-art performance on the SICAPv2 dataset, and the visual analysis shows the accurate prediction results of instance level.

Multiple Instance Learning whole slide images

RCMNet: A deep learning model assists CAR-T therapy for leukemia

no code implementations6 May 2022 Ruitao Zhang, Xueying Han, Ijaz Gul, Shiyao Zhai, Ying Liu, Yongbing Zhang, Yuhan Dong, Lan Ma, Dongmei Yu, Jin Zhou, Peiwu Qin

Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset.

Image Classification Transfer Learning

Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference

no code implementations6 May 2022 Yang Liu, Ersi Zhang, Lulu Xu, Chufan Xiao, Xiaoyun Zhong, Lijin Lian, Fang Li, Bin Jiang, Yuhan Dong, Lan Ma, Qiming Huang, Ming Xu, Yongbing Zhang, Dongmei Yu, Chenggang Yan, Peiwu Qin

Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.

Computed Tomography (CT) Image Segmentation +3

PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI

1 code implementation24 Apr 2022 Jingfen Xie, Jian Zhang, Yongbing Zhang, Xiangyang Ji

Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i. e., where to sample and how to reconstruct.


HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging

1 code implementation CVPR 2022 Xuanyu Zhang, Yongbing Zhang, Ruiqin Xiong, Qilin Sun, Jian Zhang

Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine.

Compressive Sensing

Freshness-Optimal Caching for Information Updating Systems with Limited Cache Storage Capacity

no code implementations28 Dec 2020 Haibin Xie, Minquan Cheng, Yongbing Zhang

In order to keep the average freshness as large as possible in the cache updating system, we formulate an average freshness-optimal cache updating problem (AFOCUP) to obtain an optimal cache scheme.

Information Theory Information Theory

Depth image denoising using nuclear norm and learning graph model

no code implementations9 Aug 2020 Chenggang Yan, Zhisheng Li, Yongbing Zhang, Yutao Liu, Xiangyang Ji, Yongdong Zhang

The depth images denoising are increasingly becoming the hot research topic nowadays because they reflect the three-dimensional (3D) scene and can be applied in various fields of computer vision.

Image Denoising Image Restoration

Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction

1 code implementation22 Feb 2020 Jiangpeng Yan, Shuo Chen, Yongbing Zhang, Xiu Li

Our proposed method can reach a better trade-off between computation cost and reconstruction performance for MR reconstruction problem with good generalizability and offer insights to design neural networks for other medical image applications.

Image Reconstruction Neural Architecture Search +1

PgNN: Physics-guided Neural Network for Fourier Ptychographic Microscopy

no code implementations19 Sep 2019 Yongbing Zhang, Yangzhe Liu, Xiu Li, Shaowei Jiang, Krishna Dixit, Xinfeng Zhang, Xiangyang Ji

Since the optimal parameters of the PgNN can be derived by minimizing the difference between the model-generated images and real captured angle-varied images corresponding to the same scene, the proposed PgNN can get rid of the problem of massive training data as in traditional supervised methods.

On the Mathematical Understanding of ResNet with Feynman Path Integral

no code implementations16 Apr 2019 Minghao Yin, Xiu Li, Yongbing Zhang, Shiqi Wang

In this paper, we aim to understand Residual Network (ResNet) in a scientifically sound way by providing a bridge between ResNet and Feynman path integral.

Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks

1 code implementation3 Sep 2018 Yuan Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, Liang Lin

We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.

Image Super-Resolution Image-to-Image Translation +1

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