AIMC Topic: Deep Learning

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Deep Learning: An Update for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image fea...

Tuberculosis detection in chest X-ray using Mayfly-algorithm optimized dual-deep-learning features.

Journal of X-ray science and technology
World-Health-Organization (WHO) has listed Tuberculosis (TB) as one among the top 10 reasons for death and an early diagnosis will help to cure the patient by giving suitable treatment. TB usually affects the lungs and an accurate bio-imaging scheme ...

[Object Detection Model Utilizing Deep Learning to Identify Retained Surgical Gauze in the Body on Postoperative Radiography: Phantom Study].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Foreign bodies such as a surgical gauze can be retained in the body after surgery and in some cases cannot be detected by postoperative radiography. The aim of this study was to develop an object detection model capable of postsurgical detec...

[A Study on Radiation Dermatitis Grading Support System Based on Deep Learning by Hybrid Generation Method].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Radiation dermatitis is one of the most common adverse events in patients undergoing radiotherapy. However, the objective evaluation of this condition is difficult to provide because the clinical evaluation of radiation dermatitis is made by...

Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning.

Technology in cancer research & treatment
Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-de...

Hierarchical convolutional models for automatic pneu-monia diagnosis based on X-ray images: new strategies in public health.

Annali di igiene : medicina preventiva e di comunita
CONCLUSIONS: Despite some limits, our findings support the notion that deep learning methods can be used to simplify the diagnostic process and improve disease management.

Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation.

Critical reviews in biomedical engineering
Brain tumor textures are among the most challenging features for neuroradiologists to extract from magnetic resonance images (MRIs). Exceptionally high-grade tumors such as gliomas require quick and precise diagnosis and medical intervention due to t...

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test.

Journal of Alzheimer's disease : JAD
BACKGROUND: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to deve...

Trends in Deep Learning for Property-driven Drug Design.

Current medicinal chemistry
It is more pressing than ever to reduce the time and costs for the development of lead compounds in the pharmaceutical industry. The co-occurrence of advances in high-throughput screening and the rise of deep learning (DL) have enabled the developmen...