AIMC Topic: Deep Learning

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Accurate Identification of the Trabecular Meshwork under Gonioscopic View in Real Time Using Deep Learning.

Ophthalmology. Glaucoma
PURPOSE: Accurate identification of iridocorneal structures on gonioscopy is difficult to master, and errors can lead to grave surgical complications. This study aimed to develop and train convolutional neural networks (CNNs) to accurately identify t...

Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images.

Experimental eye research
The purpose of this study was to develop an automatic deep learning-based approach and corresponding free, open-source software to perform segmentation of the Schlemm's canal (SC) lumen in optical coherence tomography (OCT) scans of living mouse eyes...

Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time an...

Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.

Medical physics
PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation...

Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction.

Computational and mathematical methods in medicine
The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of f...

Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance.

European journal of nuclear medicine and molecular imaging
PURPOSE: This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresp...

Potential diagnosis of COVID-19 from chest X-ray and CT findings using semi-supervised learning.

Physical and engineering sciences in medicine
COVID-19 is an infectious disease, which has adversely affected public health and the economy across the world. On account of the highly infectious nature of the disease, rapid automated diagnosis of COVID-19 is urgently needed. A few recent findings...

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study.

Laboratory investigation; a journal of technical methods and pathology
Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magn...

Training method and system for stress management and mental health care of managers based on deep learning.

Mathematical biosciences and engineering : MBE
In recent years, with the rapid development of the economy, in order to stabilize in the market and expand their own business, various companies in the form of various indicators, tangible or intangible to improve the management of the work of worker...

MARnet: multi-scale adaptive residual neural network for chest X-ray images recognition of lung diseases.

Mathematical biosciences and engineering : MBE
Chest X-ray image is an important clinical diagnostic reference to lung diseases that is a serious threat to human health. At present, with the rapid development of computer vision and deep learning technology, many scholars have carried out the frui...