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Multimodal Sentiment Analysis with Mutual Information-based Disentangled Representation Learning
Hao Sun;Ziwei Niu;Hongyi Wang;Xinyao Yu;Jiaqing Liu;Yen-Wei Chen;Lanfen Lin
IEEE Transactions on Affective Computing
Year: 2025 | Early Access Article |
Multimodal sentiment analysis seeks to utilize various types of signals to identify underlying emotions and sentiments. A key challenge in this field lies in multimodal representation learning, which aims to develop effective methods for integrating multimodal features into cohesive representations. Recent advancements include two notable approaches: one focuses on decomposing multimodal features ...Show More
SPA: Leveraging the SAM with Spatial Priors Adapter for Enhanced Medical Image Segmentation
Jihong Hu;Yinhao Li;Rahul Kumar Jain;Lanfen Lin;Yen-wei Chen
IEEE Journal of Biomedical and Health Informatics
Year: 2025 | Early Access Article |
The Segment Anything Model (SAM) has gained renown for its success in image segmentation, benefiting significantly from its pretraining on extensive datasets and its interactive prompt-based segmentation approach. Although highly effective in natural (real-world) image segmentation tasks, the SAM model encounters significant challenges in medical imaging due to the inherent differences between the...Show More
A Sentiment Pre-trained Text-Guided Multimodal Cross-Attention Transformer for Improved Depression Detection
Shiyu Teng;Shurong Chai;Jiaqing Liu;Tomoko Tateyama;Lanfen Lin;Yen-Wei Chen
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Depression is a widespread mental health issue requiring efficient automated detection methods. Traditional single-modality approaches are less effective due to the disorder’s complexity, leading to a focus on multimodal analysis. Recent advancements include transformer-based fusion methods, yet their application in depression detection is often limited by the dominant text modality. To address th...Show More
UPMatch: Enhancing Semi-Supervised Medical Image Classification through Contrastive Learning with Unreliable Pseudo Labels
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
Pseudo-labeling based semi-supervised learning (SSL) framework has proven highly successful in medical image analysis (MIA) by addressing the problem of a shortage of labeled samples. However, the existing SSL methods use a fixed or flexible confidence threshold to filter reliable samples, leaving large number of unlabeled samples unused. This is a more serious issue in MIA because of the low inte...Show More
UPMatch: Enhancing Semi-Supervised Medical Image Classification through Contrastive Learning with Unreliable Pseudo Labels
Year: 2024 | Conference Paper |
Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer
Yang Ai;Xiuju Du;Jing Liu;Yinhao Li;Lanfen Lin;Jingsong Li;Yen-Wei Chen
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinic...Show More
STFormer: Learning to Explore Spot Relationships for Spatial Transcriptomics Prediction from Histology of Colorectal Cancer
Gan Zhan;Xiuju Du;Jing Liu;Yinhao Li;Lanfen Lin;Jingsong Li;Yen-Wei Chen
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or faili...Show More
GANs-guided Conditional Diffusion Model for Synthesizing Contrast-enhanced Computed Tomography Images
Yulin Yang;Jing Liu;Qingqing Chen;Yinhao Li;Xian-Hua Han;Hongjie Hu;Lanfen Lin;Yen-Wei Chen
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
In contrast to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans can highlight discrepancies between abnormal and normal areas, commonly used in clinical diagnosis of focal liver lesions. However, the use of contrast agents in CE-CT scans imposes significant physical and economic burdens on patients in clinical practice. Recently, Generative Adversarial Networks (GANs...Show More
k-Uniform Hypergraph Reasoning Network for Focal Liver Lesions Classification in Multimodal Magnetic Resonance Imaging
Shaocong Mo;Ming Cai;Lanfen Lin;Ruofeng Tong;Fang Wang;Qingqing Chen;Hongjie Hu;Yinhao Li;Yen-Wei Chen
2024 IEEE 13th Global Conference on Consumer Electronics (GCCE)
Year: 2024 | Conference Paper |
Accurately identifying of focal liver lesions (FLLs) is crucial for liver diagnostics. In clinical, multimodal magnetic resonance imaging (MRI) offers comprehensive visual patterns vital for differentiating various liver tumors. In this paper, we introduce a uniform hypergraph based reasoning network for the classification of FLLs in multimodal MRI. Our model employs a shared-weight encoder for fe...Show More
SAMA: A Self-and-Mutual Attention Network for Accurate Recurrence Prediction of Non-Small Cell Lung Cancer Using Genetic and CT Data
Yang Ai;Jing Liu;Yinhao Li;Fang Wang;Xiuju Du;Rahul Kumar Jain;Lanfen Lin;Yen-Wei Chen
IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Early Access Article |
Cited by: Papers (1)
Accurate preoperative recurrence prediction for non-small cell lung cancer (NSCLC) is a challenging issue in the medical field. Existing studies primarily conduct image and molecular analyses independently or directly fuse multimodal information through radiomics and genomics, which fail to fully exploit and effectively utilize the highly heterogeneous cross-modal information at different levels a...Show More
Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models
Huimin Huang;Yawen Huang;Lanfen Lin;Ruofeng Tong;Yen-Wei Chen;Hao Zheng;L. Yuexiang;Yefeng Zheng
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Multi-task visual scene understanding aims to leverage the relationships among a set of correlated tasks, which are solved simultaneously by embedding them within a unified network. However, most existing methods give rise to two primary concerns from a task-level perspective: (1) the lack of task-independent correspondences for distinct tasks, and (2) the neglect of explicit task-consensual depen...Show More
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;Ruofeng Tong;Yen-Wei Chen;Hongjie Hu;Lanfen Lin;Hao Chen
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally adopt a two-stage approach, comprising a non-learnable feature embedding stage and a classifier training stage. Though it can greatly reduce memory co...Show More
Segmentation Guided Crossing Dual Decoding Generative Adversarial Network for Synthesizing Contrast-Enhanced Computed Tomography Images
Yulin Yang;Qingqing Chen;Yinhao Li;Fang Wang;Xian-Hua Han;Yutaro Iwamoto;Jing Liu;Lanfen Lin;Hongjie Hu;Yen-Wei Chen
Although contrast-enhanced computed tomography (CE-CT) images significantly improve the accuracy of diagnosing focal liver lesions (FLLs), the administration of contrast agents imposes a considerable physical burden on patients. The utilization of generative models to synthesize CE-CT images from non-contrasted CT images offers a promising solution. However, existing image synthesis models tend to...Show More
IRLSG: Invariant Representation Learning for Single-Domain Generalization in Medical Image Segmentation
Ziwei Niu;Hao Sun;Shuyi Ouyang;Shiao Xie;Yen-wei Chen;Ruofeng Tong;Lanfen Lin
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Single-domain generalization (SDG) can efficiently enhance model generalization while avoiding high annotation costs and privacy concerns. However, existing SDG methods are mainly based on data manipulation and meta-learning, which are not efficient enough due to the limited generalization performance and complex inference. In response to these challenges, we present a novel single domaininvariant...Show More
IRLSG: Invariant Representation Learning for Single-Domain Generalization in Medical Image Segmentation
Ziwei Niu;Hao Sun;Shuyi Ouyang;Shiao Xie;Yen-wei Chen;Ruofeng Tong;Lanfen Lin
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Multi-Modal and Multi-Task Depression Detection with Sentiment Assistance
Shiyu Teng;Shurong Chai;Jiaqing Liu;Tomoko Tateyama;Lanfen Lin;Yen-Wei Chen
2024 IEEE International Conference on Consumer Electronics (ICCE)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Depression, a multifaceted mental health disorder, is characterized by persistent feelings of sorrow, hopelessness, and a pervasive loss of interest or pleasure in once-enjoyed activities. It often manifests with physical and cognitive symptoms, including alterations in appetite and sleep patterns, overwhelming fatigue, difficulty in maintaining focus, and recurrent contemplations of death or suic...Show More
Knowledge Distillation-Based Domain-Invariant Representation Learning for Domain Generalization
Ziwei Niu;Junkun Yuan;Xu Ma;Yingying Xu;Jing Liu;Yen-Wei Chen;Ruofeng Tong;Lanfen Lin
Domain generalization (DG) aims to generalize the knowledge learned from multiple source domains to unseen target domains. Existing DG techniques can be subsumed under two broad categories, i.e., domain-invariant representation learning and domain manipulation. Nevertheless, it is extremely difficult to explicitly augment or generate the unseen target data. And when source domain variety increases...Show More
Spatial Attention-Guided Generative Adversarial Network for Synthesizing Contrast-enhanced Computed Tomography Images
Yulin Yang;Yinhao Li;Qingqing Chen;Xian-Hua Han;Jing Liu;Lanfen Lin;Hongjie Hu;Yen-Wei Chen
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Compared to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans provide more abundant information about focal liver lesions (FLLs), which play a crucial role in the FLLs diagnosis. However, CE-CT scans require patient to inject contrast agent into the body, which increase the physical and economic burden of the patient. In this paper, we propose a spatial attention-guid...Show More
Vision-Guided Attention-Enhanced Network for Predicting Microvascular Invasion in Hepatocellular Carcinoma
Jing Liu;Jing Ma;Yang Ai;Jiayue Zhao;Fang Wang;Lanfen Lin;Ruofeng Tong;Yen-Wei Chen;Jingsong Li
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2023 | Conference Paper |
As the most common malignant tumor worldwide, hepatocellular carcinoma (HCC) has a high rate of death and recurrence, and microvascular invasion (MVI) is considered to be an independent risk factor affecting its early recurrence and poor survival rate. Accurate preoperative prediction of MVI is of great significance for the formulation of individualized treatment plans and long-term prognosis asse...Show More
Unsupervised Domain Adaptation Using Fourier Phase Enhanced Training Images for Liver Tumors Detection in Multi-phase CT Images
Rahul Kumar Jain;Takahiro Sato;Ahmed M. El-Sayed;Taro Watasue;Tomohiro Nakagawa;Yutaro Iwamoto;Yinhao Li;Xianhua Han;Lanfen Lin;Hongjie Hu;Xiang Ruan;Yen-Wei Chen
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Computer-aided diagnostic methods, such as automatic and precise liver tumor detection, have a significant impact on healthcare. In recent years, deep learning-based liver tumor detection methods in multi-phase computed tomography (CT) images have achieved noticeable performance. Deep learning frameworks require a substantial amount of annotated training data but obtaining enough training data wit...Show More
Unsupervised Domain Adaptation Using Fourier Phase Enhanced Training Images for Liver Tumors Detection in Multi-phase CT Images
Rahul Kumar Jain;Takahiro Sato;Ahmed M. El-Sayed;Taro Watasue;Tomohiro Nakagawa;Yutaro Iwamoto;Yinhao Li;Xianhua Han;Lanfen Lin;Hongjie Hu;Xiang Ruan;Yen-Wei Chen
Year: 2023 | Conference Paper |
MVI-Wise GAN: Synthetic MRI to Improve Microvascular Invasion Prediction in Hepatocellular Carcinoma
Jing Liu;Yulin Yang;Yang Ai;Titinunt Kitrungrotsakul;Fang Wang;Lanfen Lin;Ruofeng Tong;Yen-Wei Chen;Jingsong Li
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2023 | Conference Paper |
MRI is crucial for the diagnosis of HCC patients, especially when combined with CT images for MVI prediction, richer complementary information can be learned. Many studies have shown that whether hepatocellular carcinoma is accompanied by vascular invasion can be evidenced by imaging examinations such as CT or MR, so they can be used as a multimodal joint prediction to improve the prediction accur...Show More
Contrastive Learning for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Liver CT Image and Tumor Mask
Yingying Xu;Jiayue Zhao;Qingqing Chen;Fang Wang;Lanfen Lin;Hongjie Hu;Ruofeng Tong;Jingsong Li;Yen-Wei Chen
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2023 | Conference Paper |
High early recurrence (ER) rate is the main factor leading to the poor outcome of patients with hepatocellular carcinoma (HCC). Accurate preoperative prediction of ER is thus highly desired for HCC treatment. Many radiomics solutions have been proposed for the preoperative prediction of HCC using CT images based on machine learning and deep learning methods. Nevertheless, most current radiomics ap...Show More
A Self-Supervised Transformer-Based Model for Early Recurrence Prediction of HCC with Multimodal MRI
Xianru Zhang;Gan Zhan;Fang Wang;Yinhao Li;Lanfen Lin;Hongjie Hu;Yen-Wei Chen
2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Hepatocellular carcinoma (HCC) is a representative primary liver cancer with high incidence and mortality. Surgical resection is the first option of treatment, but patients are usually at a high risk of tumor recurrence within the first 2 years, resulting in poor overall outcomes. Thus, it is of great clinical significance to predict HCC early recurrence to improve the survival rate. Existing tran...Show More
Integrating Spatial Prior Adapter for Enhancing SAM Performance in Medical Image Segmentation
2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The Segment Anything Model (SAM) has emerged as a focal point of interest within the domain of image segmentation. Capitalizing on extensive pretraining with vast datasets and an interactive prompting mechanism, SAM stands out as a formidable base model for natural image segmentation tasks. However, a number of empirical studies indicate that SAM's performance diminishes substantially when applied...Show More
MedFCT: A Frequency Domain Joint CNN-Transformer Network for Semi-supervised Medical Image Segmentation
2023 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Semi-supervised learning(SSL) is a data-efficient way in leveraging large-scale data without annotations and alleviating the dependence on labeled data. Mean-Teacher (MT) scheme with teacher-student model architecture has shown its effectiveness in semi-supervised medical image segmentation, where the student network learns from the teacher by minimizing pixel-wise consistency loss. However, exist...Show More
SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation
Huimin Huang;Shiao Xie;Lanfen Lin;Ruofeng Tong;Yen-Wei Chen;Yuexiang Li;Hong Wang;Yawen Huang;Yefeng Zheng
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (10)
Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples. These successes concentrate on the pixel-wise consistency by using convolutional neural networks (CNNs) but fail to address both global learning capability and class-level features for unlabeled data. Recent works raise a new trend that Trans...Show More
Adaptive Decomposition and Shared Weight Volumetric Transformer Blocks for Efficient Patch-Free 3D Medical Image Segmentation
Hongyi Wang;Yingying Xu;Qingqing Chen;Ruofeng Tong;Yen-Wei Chen;Hongjie Hu;Lanfen Lin
High resolution (HR) 3D medical image segmentation is vital for an accurate diagnosis. However, in the field of medical imaging, it is still a challenging task to achieve a high segmentation performance with cost-effective and feasible computation resources. Previous methods commonly use patch-sampling to reduce the input size, but this inevitably harms the global context and decreases the model's...Show More
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