AIMC Topic:
Image Interpretation, Computer-Assisted

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Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.

Computer methods and programs in biomedicine Jun 1, 2025
BACKGROUND AND OBJECTIVE: In medical image analysis, combining multiple imaging modalities enhances diagnostic accuracy by providing complementary information. However, missing modalities are common in clinical settings, limiting the effectiveness of...
Predictive Learning Models Image Processing, Computer-Assisted Male Classification Algorithms Multimodal Imaging Ultrasonography Liver Neoplasms Humans Female Area Under Curve Data Accuracy Image Interpretation, Computer-Assisted
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Self-supervised multi-modality learning for multi-label skin lesion classification.

Computer methods and programs in biomedicine Jun 1, 2025
BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide detailed views of surface structures, while clinical images offer complementary macroscopic information. Clini...
Dermoscopy Diagnosis, Computer-Assisted Deep Learning Image Interpretation, Computer-Assisted Cluster Analysis Supervised Machine Learning Humans Neural Networks, Computer Algorithms Melanoma Skin Neoplasms
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Generating Synthetic T2*-Weighted Gradient Echo Images of the Knee with an Open-source Deep Learning Model.

Academic radiology Jun 1, 2025
RATIONALE AND OBJECTIVES: Routine knee MRI protocols for 1.5 T and 3 T scanners, do not include T2*-w gradient echo (T2*W) images, which are useful in several clinical scenarios such as the assessment of cartilage, synovial blooming (deposition of he...
Signal-To-Noise Ratio Deep Learning Female Magnetic Resonance Imaging Adolescent Adult Humans Knee Joint Child Male Image Interpretation, Computer-Assisted
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Weakly supervised nuclei segmentation based on pseudo label correction and uncertainty denoising.

Artificial intelligence in medicine Jun 1, 2025
Nuclei segmentation plays a vital role in computer-aided histopathology image analysis. Numerous fully supervised learning approaches exhibit amazing performance relying on pathological image with precisely annotations. Whereas, it is difficult and t...
Humans Algorithms Female Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Cell Nucleus Cluster Analysis Uncertainty Supervised Machine Learning
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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 Jun 1, 2025
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...
Image Interpretation, Computer-Assisted Brain Female Male Child Magnetic Resonance Imaging Neural Networks, Computer Algorithms Autism Spectrum Disorder Humans
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Cross- and Intra-Image Prototypical Learning for Multi-Label Disease Diagnosis and Interpretation.

IEEE transactions on medical imaging Jun 1, 2025
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 ...
Machine Learning Algorithms Image Interpretation, Computer-Assisted Humans
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MedKAFormer: When Kolmogorov-Arnold Theorem Meets Vision Transformer for Medical Image Representation.

IEEE journal of biomedical and health informatics Jun 1, 2025
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...
Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms
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Completed Feature Disentanglement Learning for Multimodal MRIs Analysis.

IEEE journal of biomedical and health informatics Jun 1, 2025
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...
Humans Magnetic Resonance Imaging Machine Learning Brain Multimodal Imaging Image Interpretation, Computer-Assisted Algorithms Female Male
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Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

IEEE journal of biomedical and health informatics Jun 1, 2025
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 ...
Humans Brain Neoplasms Machine Learning Glioma Image Interpretation, Computer-Assisted Databases, Factual Multiple-Instance Learning Algorithms Brain
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Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy.

IEEE journal of biomedical and health informatics Jun 1, 2025
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...
Deep Learning Supervised Machine Learning Humans Image Interpretation, Computer-Assisted Algorithms
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