AIMC Topic: Image Interpretation, Computer-Assisted

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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...

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...

Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

IEEE journal of biomedical and health informatics
Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for ...

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...

FeaInfNet: Diagnosis of Medical Images With Feature-Driven Inference and Visual Explanations.

IEEE journal of biomedical and health informatics
Interpretable deep-learning models have received widespread attention in the field of image recognition. However, owing to the coexistence of medical-image categories and the challenge of identifying subtle decision-making regions, many proposed inte...

Boundary-Enhanced $U^{2}$-Net for Simultaneous Four-Chamber Segmentation in Transthoracic Echocardiography.

IEEE journal of biomedical and health informatics
The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single ventricle. Transthoracic echocardiography provides real-time estimations of cardiac function and enables...

Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound Image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency.

IEEE journal of biomedical and health informatics
Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasoun...