AIMC Topic: Deep Learning

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Endoluminal larynx anatomy model - towards facilitating deep learning and defining standards for medical images evaluation with artificial intelligence algorithms.

Otolaryngologia polska = The Polish otolaryngology
The pioneering nature of this work covers the answers to two questions: (1) Is an up-to-date anatomical model of the larynx needed for modern endoscopic diagnostics, and (2) can such a digital segmentation model be utilized for deep learning purposes...

Homologues not needed: Structure prediction from a protein language model.

Structure (London, England : 1993)
Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Weißenow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a p...

Investigating Morphologic Correlates of Driver Gene Mutation Heterogeneity via Deep Learning.

Cancer research
Despite the crucial role of phenotypic and genetic intratumoral heterogeneity in understanding and predicting clinical outcomes for patients with cancer, computational pathology studies have yet to make substantial steps in this area. The major limit...

Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.

Cancer research
UNLABELLED: Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitou...

Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Cell reports
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...

Deep Learning for Synthetic CT from Bone MRI in the Head and Neck.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Bone MR imaging techniques enable visualization of cortical bone without the need for ionizing radiation. Automated conversion of bone MR imaging to synthetic CT is highly desirable for downstream image processing and eventual...

[Effect of Deep Learning-based Contrast-enhanced CT Image Reconstruction on the Image Quality of the Biliary System].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
Objective To evaluate the effect of a deep learning reconstruction (DLR) method on the visibility of contrast-enhanced CT images of the biliary system by comparing it with different iterative reconstruction algorithms including the adaptive iterative...

Difference imaging from single measurements in diffuse optical tomography: a deep learning approach.

Journal of biomedical optics
SIGNIFICANCE: "Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measureme...

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning.

Translational vision science & technology
PURPOSE: Standard automated perimetry is the gold standard to monitor visual field (VF) loss in glaucoma management, but it is prone to intrasubject variability. We trained and validated a customized deep learning (DL) regression model with Xception ...