Cardiovascular

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet.

Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th most commonly occurring cancer among the other cancers in the human society, such that about 300,000 new cases were found in 2018. While cancer diagnosis is based on interventional methods such as surgery, radiotherapy, and chemotherapy, studies show th...

Learning-based occupational x-ray scatter estimation.

During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, ...

A hybrid deep learning model for forecasting lymphocyte depletion during radiation therapy.

PURPOSE: Recent studies have shown that severe depletion of the absolute lymphocyte count (ALC) induced by radiation therapy (RT) has been associated ...

Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of ...

Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network.

PURPOSE: Image guidance is used to improve the accuracy of radiation therapy delivery but results in increased dose to patients. This is of particular...

Automatic Segmentation of Magnetic Resonance Images of Severe Patients with Advanced Liver Cancer and the Molecular Mechanism of Emodin-Induced Apoptosis of HepG2 Cells under the Deep Learning.

To improve the accuracy of clinical diagnosis of severe patients with advanced liver cancer and enhance the effect of chemotherapy treatment, the U-Ne...

Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction.

OBJECTIVE: To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstruct...

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.

The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accura...

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of mach...

Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.

In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic a...

Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.

Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images w...

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process re...

Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study.

To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced tech...

Deep Learning Capabilities for the Categorization of Microcalcification.

Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporti...

Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine.

Gastric cancer is still the fifth most common malignant tumor in the world and has the fourth highest mortality rate in the world. Gastric cancer is d...

From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer.

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine ...

Editorial Comment to Impact of neoadjuvant chemotherapy on survival and recurrence patterns after robot-assisted radical cystectomy for muscle-invasive bladder cancer: Results from the International Robotic Cystectomy Consortium.

Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.

Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of i...

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.

In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the...

Clinical evaluation of two AI models for automated breast cancer plan generation.

BACKGROUND: Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow du...