152 citations · 423 across the 28 of their papers we have counts for
28 papers
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
Jiahua Dong, Yue Zhang, Qiuli Wang +7
Medical image segmentation is crucial in the field of medical imaging, aiding in disease diagnosis and surgical planning. Most established segmentation methods rely on supervised d…
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification
Shuyi Ouyang, Hongyi Wang, Ziwei Niu +6
The task of multi-label image classification involves recognizing multiple objects within a single image. Considering both valuable semantic information contained in the labels and…
A Survey on Domain Generalization for Medical Image Analysis
Ziwei Niu, Shuyi Ouyang, Shiao Xie +2
Medical Image Analysis (MedIA) has emerged as a crucial tool in computer-aided diagnosis systems, particularly with the advancement of deep learning (DL) in recent years. However,…
Memory-Inspired Temporal Prompt Interaction for Text-Image Classification
Xinyao Yu, Hao Sun, Ziwei Niu +4
In recent years, large-scale pre-trained multimodal models (LMM) generally emerge to integrate the vision and language modalities, achieving considerable success in various natural…
M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images
Hongyi Wang, Xiuju Du, Jing Liu +3
The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable i…
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher
Hongyi Wang, Luyang Luo, Fang Wang +5
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associat…
Modality-invariant and Specific Prompting for Multimodal Human Perception Understanding
Hao Sun, Ziwei Niu, Xinyao Yu +3
Understanding human perceptions presents a formidable multimodal challenge for computers, encompassing aspects such as sentiment tendencies and sense of humor. While various method…
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Jiahua Dong, Guohua Cheng, Yue Zhang +5
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep…
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
Hongyi Wang, Luyang Luo, Fang Wang +5
Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually r…
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance
Hongyi Wang, Lanfen Lin, Hongjie Hu +6
High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3…
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Considerable progress has been made in domain generalization (DG) which aims to learn a generalizable model from multiple well-annotated source domains to unknown target domains. H…
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation
Huimin Huang, Shiao Xie1, Lanfen Lin +4
Recently, a variety of vision transformers have been developed as their capability of modeling long-range dependency. In current transformer-based backbones for medical image segme…
CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation
Hao Sun, Hongyi Wang, Jiaqing Liu +2
Multimodal sentiment analysis and depression estimation are two important research topics that aim to predict human mental states using multimodal data. Previous research has focus…
Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer
Yinao Li, Yutaro Iwamoto, Ryousuke Nakamura +3
Recently, convolutional neural networks (CNN) have obtained promising results in single-image SR for hyperspectral pansharpening. However, enhancing CNNs' representation ability wi…
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation
Xu Ma, Junkun Yuan, Yen-wei Chen +2
Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a labeled source domain and adapts a trained model to an unlabeled target domain. To bridge the gap b…
Mixed Transformer U-Net For Medical Image Segmentation
Hongyi Wang, Shiao Xie, Lanfen Lin +4
Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers h…
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. The existing DG methods usually exploit the…
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Junkun Yuan, Xu Ma, Ruoxuan Xiong +5
Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representation…
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Conventional Domain Generalization (CDG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However, due to expensive annotation co…
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
Yue Zhang, Chengtao Peng, Liying Peng +8
Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usuall…
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
Junkun Yuan, Anpeng Wu, Kun Kuang +4
Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confo…
Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small-Cell Lung Cancer
Panyanat Aonpong, Yutaro Iwamoto, Xian-Hua Han +2
Non-small cell lung cancer (NSCLC) is a serious disease and has a high recurrence rate after the surgery. Recently, many machine learning methods have been proposed for recurrence…
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Huimin Huang, Ming Cai, Lanfen Lin +9
Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed fo…
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
Yingying Xu, Ming Cai, Lanfen Lin +10
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is new…