AIMC Topic: Image Interpretation, Computer-Assisted
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Semi-supervised medical image segmentation via weak-to-strong perturbation consistency and edge-aware contrastive representation.
Medical image analysis
Jan 6, 2025
Despite that supervised learning has demonstrated impressive accuracy in medical image segmentation, its reliance on large labeled datasets poses a challenge due to the effort and expertise required for data acquisition. Semi-supervised learning has ...
Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.
BMC medical imaging
Jan 6, 2025
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their i...
Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.
Scientific reports
Jan 6, 2025
This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such...
Artificial Intelligence in Gastrointestinal Imaging: Advances and Applications.
Radiologic clinics of North America
Jan 4, 2025
While artificial intelligence (AI) has shown considerable progress in many areas of medical imaging, applications in abdominal imaging, particularly for the gastrointestinal (GI) system, have notably lagged behind advancements in other body regions. ...
Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network.
Medical image analysis
Jan 4, 2025
This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (US) images using artificial neural networks. The HCC-Net integrates the discrete wavelet transform (...
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.
Medical image analysis
Jan 4, 2025
Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely available diagnostic aid that provides functional information of the heart. However, its ability to classif...
Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects.
Best practice & research. Clinical gastroenterology
Jan 4, 2025
The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and management of biliopancreatic lesions. This review highlights the current state of AI-enhanced EUS (AI-E...
Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.
Scientific reports
Jan 4, 2025
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindicati...
Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology.
Medical image analysis
Jan 3, 2025
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide...
Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound.
International journal of computer assisted radiology and surgery
Jan 3, 2025
PURPOSE: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and ca...
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