AIMC Topic: Deep Learning

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Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer.

Clinical hemorheology and microcirculation
OBJECTIVES: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC).

A deep learning-based recognition for dangerous objects imaged in X-ray security inspection device.

Journal of X-ray science and technology
Several limitations in algorithms and datasets in the field of X-ray security inspection result in the low accuracy of X-ray image inspection. In the literature, there have been rare studies proposed and datasets prepared for the topic of dangerous o...

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Methods in molecular biology (Clifton, N.J.)
Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have gre...

Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.

Combinatorial chemistry & high throughput screening
IMPORTANCE: Accurate pre-treatment prediction of distant metastasis in patients with Nasopharyngeal Carcinoma (NPC) enables the implementation of appropriate treatment strategies for high-risk individuals.

A deep learning-based method for cervical transformation zone classification in colposcopy images.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Colposcopy is one of the common methods of cervical cancer screening. The type of cervical transformation zone is considered one of the important factors for grading colposcopic findings and choosing treatment.

Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches.

Combinatorial chemistry & high throughput screening
BACKGROUND: Osteoporosis is a prevalent disease for the aged population. Chinese herbderived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throu...

Deep Learning Based COVID-19 Detection Using Medical Images: Is Insufficient Data Handled Well?

Current medical imaging
Deep learning is a prominent method for automatic detection of COVID-19 disease using a medical dataset. This paper aims to give a perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extens...

Evaluation of Image Classification for Quantifying Mitochondrial Morphology Using Deep Learning.

Endocrine, metabolic & immune disorders drug targets
BACKGROUND: Mitochondrial morphology reversibly changes between fission and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, ...

Quality Assurance based on Deep Learning for Pelvic OARs Delineation in Radiotherapy.

Current medical imaging
BACKGROUND: Correct delineation of organs at risk (OARs) is an important step for radiotherapy and it is also a time-consuming process that depends on many factors.

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray.

Current medical imaging
BACKGROUND: The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detect...