AIMC Topic: Algorithms

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A Trusted Medical Image Zero-Watermarking Scheme Based on DCNN and Hyperchaotic System.

IEEE journal of biomedical and health informatics
The zero-watermarking methods provide a means of lossless, which was adopted to protect medical image copyright requiring high integrity. However, most existing studies have only focused on robustness and there has been little discussion about the an...

A Hierarchical Graph Convolutional Network With Infomax-Guided Graph Embedding for Population-Based ASD Detection.

IEEE journal of biomedical and health informatics
Recently, functional magnetic resonance imaging (fMRI)-based brain networks have been shown to be an effective diagnostic tool with great potential for accurately detecting autism spectrum disorders (ASD). Meanwhile, the successful use of graph convo...

Cross- and Intra-Image Prototypical Learning for Multi-Label Disease Diagnosis and Interpretation.

IEEE transactions on medical imaging
Recent advances in prototypical learning have shown remarkable potential to provide useful decision interpretations associating activation maps and predictions with class-specific training prototypes. Such prototypical learning has been well-studied ...

MedKAFormer: When Kolmogorov-Arnold Theorem Meets Vision Transformer for Medical Image Representation.

IEEE journal of biomedical and health informatics
Vision Transformers (ViTs) suffer from high parameter complexity because they rely on Multi-layer Perceptrons (MLPs) for nonlinear representation. This issue is particularly challenging in medical image analysis, where labeled data is limited, leadin...

GVM-Net: A GNN-Based Vessel Matching Network for 2D/3D Non-Rigid Coronary Artery Registration.

IEEE transactions on medical imaging
The registration of coronary artery structures from preoperative coronary computed tomography angiography to intraoperative coronary angiography is of great interest to improve guidance in percutaneous coronary interventions. However, non-rigid defor...

Completed Feature Disentanglement Learning for Multimodal MRIs Analysis.

IEEE journal of biomedical and health informatics
Multimodal MRIs play a crucial role in clinical diagnosis and treatment. Feature disentanglement (FD)-based methods, aiming at learning superior feature representations for multimodal data analysis, have achieved significant success in multimodal lea...

GDP-Net: Global Dependency-Enhanced Dual-Domain Parallel Network for Ring Artifact Removal.

IEEE transactions on medical imaging
In Computed Tomography (CT) imaging, the ring artifacts caused by the inconsistent detector response can significantly degrade the reconstructed images, having negative impacts on the subsequent applications. The new generation of CT systems based on...

Optimized Drug-Drug Interaction Extraction With BioGPT and Focal Loss-Based Attention.

IEEE journal of biomedical and health informatics
Drug-drug interactions (DDIs) are a significant focus in biomedical research and clinical practice due to their potential to compromise treatment outcomes or cause adverse effects. While deep learning approaches have advanced DDI extraction, challeng...

Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy.

IEEE journal of biomedical and health informatics
In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gla...

Exploring Unbiased Activation Maps for Weakly Supervised Tissue Segmentation of Histopathological Images.

IEEE transactions on medical imaging
Tissue segmentation in histopathological images plays a crucial role in computational pathology, owing to its significant potential to indicate the prognosis of cancer patients. Presently, numerous Weakly Supervised Semantic Segmentation (WSSS) metho...