AIMC Topic: Image Interpretation, Computer-Assisted

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Two-stage color fundus image registration via Keypoint Refinement and Confidence-Guided Estimation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 19, 2025
Color fundus images are widely used for diagnosing diseases such as Glaucoma, Cataracts, and Diabetic Retinopathy. The registration of color fundus images is crucial for assessing changes in fundus appearance to determine disease progression. In this...
Humans Algorithms Image Processing, Computer-Assisted Fundus Oculi Image Interpretation, Computer-Assisted
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Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images.

Scientific reports Apr 19, 2025
The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient's survival. Mammography has recently been recommended as diag...
Thermography Mammography Image Interpretation, Computer-Assisted Breast Neoplasms Deep Learning Image Processing, Computer-Assisted Humans Algorithms Female
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Lightweight Multi-Stage Aggregation Transformer for robust medical image segmentation.

Medical image analysis Apr 18, 2025
Capturing rich multi-scale features is essential to address complex variations in medical image segmentation. Multiple hybrid networks have been developed to integrate the complementary benefits of convolutional neural networks (CNN) and Transformers...
Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted
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Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.

Academic radiology Apr 18, 2025
RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recently, artificial intelligence has increasingly been applied to imaging for neurodegenerative disorders....
Putamen Parkinson Disease Image Interpretation, Computer-Assisted Early Diagnosis Machine Learning Aged Middle Aged Humans Retrospective Studies Sensitivity and Specificity Case-Control Studies Female Male Magnetic Resonance Imaging
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Tailored self-supervised pretraining improves brain MRI diagnostic models.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 17, 2025
Self-supervised learning has shown potential in enhancing deep learning methods, yet its application in brain magnetic resonance imaging (MRI) analysis remains underexplored. This study seeks to leverage large-scale, unlabeled public brain MRI datase...
Humans Algorithms Magnetic Resonance Imaging Supervised Machine Learning Image Interpretation, Computer-Assisted Brain Brain Neoplasms Deep Learning
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Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.

Tomography (Ann Arbor, Mich.) Apr 17, 2025
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...
Shoulder Edema Shoulder Joint Rotator Cuff Injuries Adult Image Interpretation, Computer-Assisted Middle Aged Female Sensitivity and Specificity Observer Variation Deep Learning Male Humans Reproducibility of Results Magnetic Resonance Imaging Prospective Studies Aged
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Assessment of AI-accelerated T2-weighted brain MRI, based on clinical ratings and image quality evaluation.

European journal of radiology Apr 16, 2025
OBJECTIVE: To compare clinical ratings and signal-to-noise ratio (SNR) measures of a commercially available Deep Learning-based MRI reconstruction method (T2) against conventional T2- turbo spin echo brain MRI (T2).
Signal-To-Noise Ratio Image Interpretation, Computer-Assisted Artifacts Brain Diseases Adult Male Middle Aged Deep Learning Adolescent Magnetic Resonance Imaging Brain Female Aged Humans
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An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.

Scientific reports Apr 16, 2025
The importance of gastric cancer (GC) and the role of deep learning techniques in categorizing GC histopathology images have recently increased. Identifying the drawbacks of traditional deep learning models, including lack of interpretability, inabil...
Stomach Neoplasms Deep Learning Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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DCATNet: polyp segmentation with deformable convolution and contextual-aware attention network.

BMC medical imaging Apr 14, 2025
Polyp segmentation is crucial in computer-aided diagnosis but remains challenging due to the complexity of medical images and anatomical variations. Current state-of-the-art methods struggle with accurate polyp segmentation due to the variability in ...
Humans Algorithms Deep Learning Colonic Polyps Image Interpretation, Computer-Assisted
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides.

Scientific reports Apr 14, 2025
Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) ...
Algorithms Image Interpretation, Computer-Assisted Female Uterine Cervical Neoplasms Deep Learning Humans Image Processing, Computer-Assisted Neural Networks, Computer Principal Component Analysis Machine Learning
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