Breast Cancer

Artificial Intelligence: An Emerging Intellectual Sword for Battling Carcinomas.

Artificial Intelligence (AI) is a branch of computer science that deals with mathematical algorithms to mimic the abilities and intellectual work performed by the human brain. Nowadays, AI is being effectively utilized in addressing difficult healthcare challenges, including complex biological abnormalities, diagnosis, treatment, and clinical prognosis of various life-threatening diseases, like ca...

Dual-domain fusion deep convolutional neural network for low-dose CT denoising.

BACKGROUND: In view of the underlying health risks posed by X-ray radiation, the main goal of the present research is to achieve high-quality CT images at the same time as reducing x-ray radiation. In recent years, convolutional neural network (CNN) has shown excellent performance in removing low-dose CT noise. However, previous work mainly focused on deepening and feature extraction work on CNN w...

Artificial intelligence 101 for veterinary diagnostic imaging.

The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This arti...

Instantaneous photosynthetically available radiation models for ocean waters using neural networks.

Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in mo...

Technological advances in body CT: a primer for beginners.

Many technological advances have entered the clinical routine of Computed Tomography (CT) imaging. The new CT scanners have specific solutions in gant...

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

BACKGROUND: Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectivel...

Artificial Intelligence for Image Registration in Radiation Oncology.

Automatic image registration plays an important role in many aspects of the radiation oncology workflow ranging from treatment simulation, image guide...

Artificial Intelligence for Outcome Modeling in Radiotherapy.

Outcome modeling plays an important role in personalizing radiotherapy and finds applications in specialized areas such as adaptive radiotherapy. Conv...

Quality Assurance for AI-Based Applications in Radiation Therapy.

Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems r...

Ethical Aspects of Artificial Intelligence in Radiation Oncology.

Radiation oncology is a field that heavily relies on new technology. Data science and artificial intelligence will have an important role in the entir...

[Comparison of Perioperative Outcomes between Open and Robot-Assisted Laparoscopic Radical Cystectomy].

We compared the perioperative outcomes of open (ORC) and robot-assisted laparoscopic radical cystectomy (RARC) for patients with bladder cancer. We re...

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurat...

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

BACKGROUND: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain con...

Complex dynamics in a Hopfield neural network under electromagnetic induction and electromagnetic radiation.

Due to the potential difference between two neurons and that between the inner and outer membranes of an individual neuron, the neural network is alwa...

A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images.

Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recen...

A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.

The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-N...

Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction.

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breas...

Deep learning methods for lesion detection on mammography images: a comparative analysis.

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially amon...

Deep Learning Methods for Detecting Side Effects of Cancer Chemotherapies Reported in a Remote Monitoring Web Application.

The objective of our work was to develop deep learning methods for extracting and normalizing patient-reported free-text side effects in a cancer chem...

Predictors of Toxicity Among Older Adults with Cancer.

An increasing number of cancer patients are of advanced age as the incidence of cancer increases with age. In this article, the clinical predictors of...