134 citations · 256 across the 14 of their papers we have counts for
14 papers
Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
Chuang Zhu, Shengjie Liu, Zekuan Yu +15
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 t…
TCNL: Transparent and Controllable Network Learning Via Embedding Human-Guided Concepts
Zhihao Wang, Chuang Zhu
Explaining deep learning models is of vital importance for understanding artificial intelligence systems, improving safety, and evaluating fairness. To better understand and contro…
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
Shengjie Liu, Chuang Zhu, Feng Xu +3
The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is cond…
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Wenkai Chen, Chuang Zhu, Yi Chen +2
Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance. Several recent effective methods for handling noisy labels have two key steps: 1) d…
Hard Sample Aware Noise Robust Learning for Histopathology Image Classification
Chuang Zhu, Wenkai Chen, Ting Peng +2
Deep learning-based histopathology image classification is a key technique to help physicians in improving the accuracy and promptness of cancer diagnosis. However, the noisy label…
Construct Informative Triplet with Two-stage Hard-sample Generation
Chuang Zhu, Zheng Hu, Huihui Dong +3
In this paper, we propose a robust sample generation scheme to construct informative triplets. The proposed hard sample generation is a two-stage synthesis framework that produces…
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Feng Xu, Chuang Zhu, Wenqi Tang +7
Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast can…
Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
Zheng Hu, Chuang Zhu, Gang He
Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled da…
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark
Shuhao Qiu, Chuang Zhu, Wenli Zhou
In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, wh…
LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
Xinyu Jia, Chuang Zhu, Minzhen Li +3
It is very challenging for various visual tasks such as image fusion, pedestrian detection and image-to-image translation in low light conditions due to the loss of effective targe…
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
Ke Mei, Chuang Zhu, Jiaqi Zou +1
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to…
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
Chuang Zhu, Ke Mei, Ting Peng +4
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly eff…
Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
Ke Mei, Chuang Zhu, Lei Jiang +2
Segmentation from renal pathological images is a key step in automatic analyzing the renal histological characteristics. However, the performance of models varies significantly in…
Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
Siyan Tao, Yao Guo, Chuang Zhu +4
In this paper, we propose a novel method for highly efficient follicular segmentation of thyroid cytopathological WSIs. Firstly, we propose a hybrid segmentation architecture, whic…