Latest AI and machine learning research in breast cancer for healthcare professionals.
Ionizing radiation has very complex biological effects, such as inducing damage to DNA and proteins...
BACKGROUND: Female carriers of a or germline mutation face a high lifetime risk to develop breast ...
We describe a novel method to achieve a universal, massive, and fully automated analysis of cell mot...
Lung malignancies have been extensively characterized through radiomics and deep learning. By provid...
Because histologic types are subjective and difficult to reproduce between pathologists, tissue morp...
The therapeutic concept of unleashing a pre-existing immune response against the tumor by the applic...
We present DeepDose, a deep learning framework for fast dose calculations in radiation therapy. Give...
OBJECTIVE: The purpose of this study was to correlate potential the stabilometric parameters of baro...
BACKGROUND: Interstitial lung disease requires frequent re-examination, which directly causes excess...
Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and ...
Ontologies are a formal, computer-compatible method for representing scientific knowledge about a gi...
Triple-negative breast cancer (TNBC) cells are deficient in estrogen, progesterone and ERBB2 recepto...
PURPOSE: In this paper, for the purpose of accurate and efficient mass detection, we propose a new d...
Early detection of breast cancer and its correct stage determination are important for prognosis and...
Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment ...
Artificial intelligence (AI) and machine learning (ML) approaches have caught the attention of many ...
The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optim...
PURPOSE: Limiting the dose to the rectum can be one of the most challenging aspects of creating a do...
OBJECTIVES: Performance of recently developed deep learning models for image classification surpasse...
The aim of this study is to explore the feasibility of using machine learning (ML) technology to pre...
Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather ...