Hypergraph-Based High-Order Correlation Analysis for Large-Scale Long-Tailed Data Classification.

High-order correlations, which capture complex interactions among multiple entities, extend beyond traditional graph representations and support a wider range of applications. However, existing neural network models for high-order correlations encounter scalability issues on large datasets due to the substantial computational complexity involved in processing large-scale structures. In addition, long-tailed distributions, which are common in real-world data, result in underrepresented categories and hinder the model's ability to learn effective high-order interaction patterns for rare instances.

Self-Supervised Hypergraph Training Framework via Structure-Aware Learning.

Hypergraphs, with their ability to model complex, beyond pair-wise correlations, presents a significant advancement over traditional graphs for capturing intricate relational data across diverse domains. However, the integration of hypergraphs into self-supervised learning (SSL) frameworks has been hindered by the intricate nature of high-order structural variations. This paper introduces the Self-Supervised Hypergraph Training Framework via Structure-Aware Learning (SS-HT), designed to enhance the perception and measurement of these variations within hypergraphs.

Kernelized Hypergraph Neural Networks.

Hypergraph Neural Networks (HGNNs) have attracted much attention for high-order structural data learning. Existing methods mainly focus on simple mean-based aggregation or manually combining multiple aggregations to capture multiple information on hypergraphs. However, those methods inherently lack continuous non-linear modeling ability and are sensitive to varied distributions.

LMS-Net: A learned Mumford-Shah network for binary few-shot medical image segmentation.

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMS-Net), for the FSS task.

HGMSurvNet: A two-stage hypergraph learning network for multimodal cancer survival prediction.

Cancer survival prediction based on multimodal data (e.g., pathological slides, clinical records, and genomic profiles) has become increasingly prevalent in recent years.

Bidirectional Projection-Based Multi-Modal Fusion Transformer for Early Detection of Cerebral Palsy in Infants.

Periventricular white matter injury (PWMI) is the most frequent magnetic resonance imaging (MRI) finding in infants with Cerebral Palsy (CP). We aim to detect CP and identify subtle, sparse PWMI lesions in infants under two years of age with immature brain structures. Based on the characteristic that the responsible lesions are located within five target regions, we first construct a multi-modal dataset including 243 cases with the mask annotations of five target regions for delineating anatomical structures on T1-Weighted Imaging (T1WI) images, masks for lesions on T2-Weighted Imaging (T2WI) images, and categories (CP or Non-CP).

Mode Hypergraph Neural Network.

The hypergraph neural network (HGNN) is an emerging powerful tool for modeling and learning complex, high-order correlations among entities upon hypergraph structures. While existing HGNN-based approaches excel in modeling high-order correlations among data using hyperedges, they often have difficulties in distinguishing diverse semantics (e.g.

Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation.

We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOLO models, while powerful, have limitations in their neck designs that restrict the integration of cross-level features and the exploitation of high-order feature interrelationships. To address these challenges, we propose the Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation.

Multiview Representation Learning With One-to-Many Dynamic Relationships.

Integrating information from multiple views to obtain potential representations with stronger expressive ability has received significant attention in practical applications. Most existing algorithms usually focus on learning either the consistent or complementary representation of views and, subsequently, integrate one-to-one corresponding sample representations between views. Although these approaches yield effective results, they do not fully exploit the information available from multiple views, limiting the potential for further performance improvement.

A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification.

Domain adaptation has demonstrated success in classification of multi-center autism spectrum disorder (ASD). However, current domain adaptation methods primarily focus on classifying data in a single target domain with the assistance of one or multiple source domains, lacking the capability to address the clinical scenario of identifying ASD in multiple target domains. In response to this limitation, we propose a Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation (TCL-MTDA) network for identifying ASD in multiple target domains.

Deep unfolding network with spatial alignment for multi-modal MRI reconstruction.

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly under-sampled k-space data with another fully-sampled reference modality is an efficient solution. However, the misalignment between modalities, which is common in clinic practice, can negatively affect reconstruction quality.

B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion.

Learning using privileged information (LUPI) has shown its effectiveness to improve the B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) by transferring knowledge from the elasticity ultrasound (EUS). However, LUPI only performs transfer learning between the paired data with shared labels, and cannot handle the scenario of modality imbalance. In order to conduct the supervised transfer learning between the paired ultrasound data together with the additional single-modal BUS images, a novel multi-view LUPI algorithm with Dual-Level Modality Completion, named DLMC-LUPI, is proposed to improve the performance of BUS-based CAD.

FEFA: Frequency Enhanced Multi-Modal MRI Reconstruction With Deep Feature Alignment.

Integrating complementary information from multiple magnetic resonance imaging (MRI) modalities is often necessary to make accurate and reliable diagnostic decisions. However, the different acquisition speeds of these modalities mean that obtaining information can be time consuming and require significant effort. Reference-based MRI reconstruction aims to accelerate slower, under-sampled imaging modalities, such as T2-modality, by utilizing redundant information from faster, fully sampled modalities, such as T1-modality.

Correction to: Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Image.

Presents corrections to the paper, Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Image.

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis.

Multimodal Co-Attention Fusion Network With Online Data Augmentation for Cancer Subtype Classification.

It is an essential task to accurately diagnose cancer subtypes in computational pathology for personalized cancer treatment. Recent studies have indicated that the combination of multimodal data, such as whole slide images (WSIs) and multi-omics data, could achieve more accurate diagnosis. However, robust cancer diagnosis remains challenging due to the heterogeneity among multimodal data, as well as the performance degradation caused by insufficient multimodal patient data.

Histopathology Image Classification With Noisy Labels via The Ranking Margins.

Clinically, histopathology images always offer a golden standard for disease diagnosis. With the development of artificial intelligence, digital histopathology significantly improves the efficiency of diagnosis. Nevertheless, noisy labels are inevitable in histopathology images, which lead to poor algorithm efficiency.

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images.

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automatic CAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models.

Hypergraph Isomorphism Computation.

The isomorphism problem, crucial in network analysis, involves analyzing both low-order and high-order structural information. Graph isomorphism algorithms focus on structural equivalence to simplify solver space, aiding applications like protein design, chemical pathways, and community detection. However, they fall short in capturing complex high-order relationships, unlike hypergraph isomorphism methods.

Time multiscale regularization for nonlinear image registration.

Regularization-based methods are commonly used for image registration. However, fixed regularizers have limitations in capturing details and describing the dynamic registration process. To address this issue, we propose a time multiscale registration framework for nonlinear image registration in this paper.

A fine-grained orthodontics segmentation model for 3D intraoral scan data.

With the widespread application of digital orthodontics in the diagnosis and treatment of oral diseases, more and more researchers focus on the accurate segmentation of teeth from intraoral scan data. The accuracy of the segmentation results will directly affect the follow-up diagnosis of dentists. Although the current research on tooth segmentation has achieved promising results, the 3D intraoral scan datasets they use are almost all indirect scans of plaster models, and only contain limited samples of abnormal teeth, so it is difficult to apply them to clinical scenarios under orthodontic treatment.

Multi-Scale Efficient Graph-Transformer for Whole Slide Image Classification.

The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness for learning multi-scale image representation, it still cannot work well on the gigapixel WSIs due to their extremely large image sizes. To this end, we propose a novel Multi-scale Efficient Graph-Transformer (MEGT) framework for WSI classification.