AIMC Topic: Image Interpretation, Computer-Assisted

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Superior skin cancer classification by the combination of human and artificial intelligence.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. Howe...

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

Medical image analysis
Deep learning networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets, particularly on metrics focused on large pathologies. For diseases such as Multiple Sclerosis (MS), however,...

Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the low intrinsic contrast between the thermal lesion and normal tissue. Deep learning, especially convolu...

Multi-task learning for quality assessment of fetal head ultrasound images.

Medical image analysis
It is essential to measure anatomical parameters in prenatal ultrasound images for the growth and development of the fetus, which is highly relied on obtaining a standard plane. However, the acquisition of a standard plane is, in turn, highly subject...

Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning.

IEEE transactions on neural networks and learning systems
Accurate segmentation of anatomical brain structures is crucial for many neuroimaging applications, e.g., early brain development studies and the study of imaging biomarkers of neurodegenerative diseases. Although multi-atlas segmentation (MAS) has a...

Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

The Journal of pathology
In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated pa...

Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.

IEEE transactions on cybernetics
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pa...