AIMC Topic: Image Interpretation, Computer-Assisted
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Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.
Journal of imaging informatics in medicine
Aug 13, 2024
Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, ADC, and FLAIR MRI sequences provide for deep learning in infarct segmentation. Recent technologies in...
Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy.
Microvascular research
Aug 13, 2024
Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and brain of living subjects. In medical imaging, edge detection is used to accurately identify and deli...
An Immunofluorescence-Guided Segmentation Model in Hematoxylin and Eosin Images Is Enabled by Tissue Artifact Correction Using a Cycle-Consistent Generative Adversarial Network.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Aug 13, 2024
Despite recent advances, the adoption of computer vision methods into clinical and commercial applications has been hampered by the limited availability of accurate ground truth tissue annotations required to train robust supervised models. Generatin...
Advancing breast ultrasound diagnostics through hybrid deep learning models.
Computers in biology and medicine
Aug 13, 2024
Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the "EfficientKNN" model, a ...
Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 12, 2024
BACKGROUND: Fully automatic analysis of myocardial perfusion cardiovascular magnetic resonance imaging datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning t...
Comparative Analysis of the Diagnostic Value of S-Detect Technology in Different Planes Versus the BI-RADS Classification for Breast Lesions.
Academic radiology
Aug 12, 2024
RATIONALE AND OBJECTIVES: S-Detect, a deep learning-based Computer-Aided Detection system, is recognized as an important tool for diagnosing breast lesions using ultrasound imaging. However, it may exhibit inconsistent findings across multiple imagin...
AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.
Abdominal radiology (New York)
Aug 12, 2024
Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pa...
Foundation models in gastrointestinal endoscopic AI: Impact of architecture, pre-training approach and data efficiency.
Medical image analysis
Aug 12, 2024
Pre-training deep learning models with large data sets of natural images, such as ImageNet, has become the standard for endoscopic image analysis. This approach is generally superior to training from scratch, due to the scarcity of high-quality medic...
Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study.
Abdominal radiology (New York)
Aug 10, 2024
BACKGROUND: In patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Cur...
Metadata-enhanced contrastive learning from retinal optical coherence tomography images.
Medical image analysis
Aug 10, 2024
Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-...
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