AIMC Topic: Image Interpretation, Computer-Assisted
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Classification of breast cancer histopathology images using a modified supervised contrastive learning method.
Medical & biological engineering & computing
Oct 30, 2024
Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically in classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often s...
Artificial Intelligence-Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography-Guided PCI: The FLASH Trial.
JACC. Cardiovascular interventions
Oct 30, 2024
BACKGROUND: Recently developed artificial intelligence-based coronary angiography (AI-QCA, fully automated) provides real-time, objective, and reproducible quantitative analysis of coronary angiography without requiring additional time or labor.
Automation
Coronary Vessels
Coronary Artery Disease
Stents
Image Interpretation, Computer-Assisted
Aged
Prospective Studies
Humans
Male
Tomography, Optical Coherence
Republic of Korea
Time Factors
Artificial Intelligence
Female
Coronary Angiography
Middle Aged
Treatment Outcome
Percutaneous Coronary Intervention
Predictive Value of Tests
Artificial intelligence-based automated determination in breast and colon cancer and distinction between atypical and typical mitosis using a cloud-based platform.
Pathology oncology research : POR
Oct 30, 2024
Artificial intelligence (AI) technology in pathology has been utilized in many areas and requires supervised machine learning. Notably, the annotations that define the ground truth for the identification of different confusing process pathologies, va...
SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.
European radiology
Oct 29, 2024
OBJECTIVES: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo ...
A Collaborative Multimodal Learning-Based Framework for COVID-19 Diagnosis.
IEEE transactions on neural networks and learning systems
Oct 29, 2024
The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mut...
Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques.
Abdominal radiology (New York)
Oct 28, 2024
Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and develop...
Multi-Class Segmentation Network Based on Tumor Tissue in Endometrial Cancer Pathology Images: ECMTrans-net.
The American journal of pathology
Oct 28, 2024
Endometrial cancer has the second highest incidence of malignant tumors in the female reproductive system. Accurate and efficient analysis of endometrial cancer pathology images is one of the important research components of computer-aided diagnosis....
WISE: Efficient WSI selection for active learning in histopathology.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 28, 2024
Deep neural network (DNN) models have been applied to a wide variety of medical image analysis tasks, often with the successful performance outcomes that match those of medical doctors. However, given that even minor errors in a model can impact pati...
Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.
European radiology
Oct 25, 2024
OBJECTIVE: To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI.
An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.
Abdominal radiology (New York)
Oct 24, 2024
An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is...
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