AIMC Topic: Deep Learning

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Deep learning only by normal brain PET identify unheralded brain anomalies.

EBioMedicine
BACKGROUND: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a mo...

Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features.

IEEE transactions on pattern analysis and machine intelligence
End-to-end distance metric learning (DML) has been applied to obtain features useful in many computer vision tasks. However, these DML studies have not provided equitable comparisons between features extracted from DML-based networks and softmax-base...

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

Cellular oncology (Dordrecht, Netherlands)
PURPOSE: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we a...

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.

Medical physics
PURPOSE: To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examina...

A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.

Medical physics
PURPOSE: Real-time image-guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve three-dimensional (3D) motion based on two-dimensional (2D) fluoroscopic images. Most common marker segmentation methods require prior...

Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules.

BioMed research international
The arterioles and venules (AV) classification of retinal vasculature is considered as the first step in the development of an automated system for analysing the vasculature biomarker association with disease prognosis. Most of the existing AV classi...

Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy.

Theranostics
Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important for larynx surgery, hence precise and rapid intraoperative histology of laryngeal tissue is crucial for providing optimal surgical outcomes. We hypothes...

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade.

Journal of the American College of Radiology : JACR
PURPOSE: Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utili...

Prediction of Protein Metal Binding Sites Using Deep Neural Networks.

Molecular informatics
Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which v...

Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior.

Physics in medicine and biology
Cervical tumor segmentation on 3D FDG PET images is a challenging task because of the proximity between cervix and bladder, both of which can uptake FDG tracers. This problem makes traditional segmentation based on intensity variation methods ineffec...