94 citations · 291 across the 30 of their papers we have counts for
23 papers · 1 filter
Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning
Guan Gui, Zhen Zhao, Lei Qi +3
In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong aug…
R2GenGPT: Radiology Report Generation with Frozen LLMs
Zhanyu Wang, Lingqiao Liu, Lei Wang +1
Large Language Models (LLMs) have consistently showcased remarkable generalization capabilities when applied to various language tasks. Nonetheless, harnessing the full potential o…
Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation
Zhen Zhao, Ye Liu, Meng Zhao +3
Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently. Due to the limited labelled data, SSMIS methods mainly focus on effectively leveragi…
Automatic Radiology Report Generation by Learning with Increasingly Hard Negatives
Bhanu Prakash Voutharoja, Lei Wang, Luping Zhou
Automatic radiology report generation is challenging as medical images or reports are usually similar to each other due to the common content of anatomy. This makes a model hard to…
Learning Partial Correlation based Deep Visual Representation for Image Classification
Saimunur Rahman, Piotr Koniusz, Lei Wang +3
Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolut…
METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens
Zhanyu Wang, Lingqiao Liu, Lei Wang +1
In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint di…
Neural Vector Fields: Implicit Representation by Explicit Learning
Xianghui Yang, Guosheng Lin, Zhenghao Chen +1
Deep neural networks (DNNs) are widely applied for nowadays 3D surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp tem…
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation
Zhen Zhao, Lihe Yang, Sifan Long +3
Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly co…
Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation
Zhen Zhao, Sifan Long, Jimin Pi +2
Recently, semi-supervised semantic segmentation has achieved promising performance with a small fraction of labeled data. However, most existing studies treat all unlabeled data eq…
A Medical Semantic-Assisted Transformer for Radiographic Report Generation
Zhanyu Wang, Mingkang Tang, Lei Wang +2
Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images.…
Instance Image Retrieval by Learning Purely From Within the Dataset
Zhongyan Zhang, Lei Wang, Yang Wang +4
Quality feature representation is key to instance image retrieval. To attain it, existing methods usually resort to a deep model pre-trained on benchmark datasets or even fine-tune…
MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization
Yue Duan, Zhen Zhao, Lei Qi +4
The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization o…
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
Shengqi Huang, Wanqi Yang, Lei Wang +2
This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA), which has not been sufficiently studied in the literature. In this setting, the…
ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease
Yuang Shi, Chen Zu, Mei Hong +6
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image ana…
Class Distribution Alignment for Adversarial Domain Adaptation
Wanqi Yang, Tong Ling, Chengmei Yang +4
Most existing unsupervised domain adaptation methods mainly focused on aligning the marginal distributions of samples between the source and target domains. This setting does not s…
Deep Learning based HEp-2 Image Classification: A Comprehensive Review
Saimunur Rahman, Lei Wang, Changming Sun +1
Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 c…
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification
Lei Qi, Lei Wang, Jing Huo +3
Unsupervised cross-domain person re-identification (Re-ID) faces two key issues. One is the data distribution discrepancy between source and target domains, and the other is the la…
Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning
Jinquan Sun, Yinghuan Shi, Yang Gao +4
Due to low tissue contrast, irregular object appearance, and unpredictable location variation, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is co…
DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition
Melih Engin, Lei Wang, Luping Zhou +1
Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition. Recent study shows that kernel…
Exploiting Structure Sparsity for Covariance-based Visual Representation
Jianjia Zhang, Lei Wang, Luping Zhou +1
The past few years have witnessed increasing research interest on covariance-based feature representation. A variety of methods have been proposed to boost its efficacy, with some…
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Luping Zhou, Lei Wang, Lingqiao Liu +2
Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite i…
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks
Zhimin Gao, Lei Wang, Luping Zhou +1
Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper presents an automatic framework for this classif…
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
Jianjia Zhang, Lei Wang, Luping Zhou +1
Stein kernel has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD m…