AIMC Topic: Breast Neoplasms

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Synthesis, characterization, antimicrobial and antimetastatic activity of silver nanoparticles synthesized from Ficus ingens leaf.

Artificial cells, nanomedicine, and biotechnology
Cancer incidence is still increasing due to inadequate responsive treatments. Inertness and biocompatibility of nanoparticles synthesized using plant extracts have shown therapeutic applications and make it to be a good anti-cancer candidates. This s...

Prediction of skin dose in low-kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry.

Medical physics
PURPOSE: The purpose of this study was to implement a machine learning model to predict skin dose from targeted intraoperative (TARGIT) treatment resulting in timely adoption of strategies to limit excessive skin dose.

Molecular and epigenetic profiles of BRCA1-like hormone-receptor-positive breast tumors identified with development and application of a copy-number-based classifier.

Breast cancer research : BCR
BACKGROUND: BRCA1-mutated cancers exhibit deficient homologous recombination (HR) DNA repair, resulting in extensive copy number alterations and genome instability. HR deficiency can also arise in tumors without a BRCA1 mutation. Compared with other ...

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images.

Biomedical engineering online
BACKGROUND: Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically...

Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a m...

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion.

Medical physics
PURPOSE: We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment p...

Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach.

Artificial intelligence in medicine
Case-Based Reasoning (CBR) is a form of analogical reasoning in which the solution for a (new) query case is determined using a database of previous known cases with their solutions. Cases similar to the query are retrieved from the database, and the...

Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation.

Journal of healthcare engineering
Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolut...

Enhancing Confusion Entropy (CEN) for binary and multiclass classification.

PloS one
Different performance measures are used to assess the behaviour, and to carry out the comparison, of classifiers in Machine Learning. Many measures have been defined on the literature, and among them, a measure inspired by Shannon's entropy named the...

Artificial Intelligence for Breast MRI in 2008-2018: A Systematic Mapping Review.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this study is to review literature from the past decade on applications of artificial intelligence (AI) to breast MRI.