AIMC Topic: Breast Neoplasms

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Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

Scientific reports
A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of bre...

Fine tuning deep learning models for breast tumor classification.

Scientific reports
This paper proposes an approach to enhance the differentiation task between benign and malignant Breast TumorsĀ (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data p...

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.

La Radiologia medica
PURPOSE: To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs).

A Review of Artificial Intelligence in Breast Imaging.

Tomography (Ann Arbor, Mich.)
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging...

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer.

Expert review of molecular diagnostics
INTRODUCTION: Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to reco...

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

Medical physics
BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatm...

Pseudo-class part prototype networks for interpretable breast cancer classification.

Scientific reports
Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key...

ERetinaNet: An Efficient Neural Network Based on RetinaNet for Mammographic Breast Mass Detection.

IEEE journal of biomedical and health informatics
Mammography is an effective method for diagnosing breast diseases, and computer-aided detection (CAD) systems play an important role in the detection of breast masses. However, low contrast and the interference of surrounding tissues make the detecti...

Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning.

Lasers in medical science
Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which ...

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

PLoS computational biology
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Ye...