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Breast Neoplasms

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Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.

PloS one
BACKGROUND AND OBJECTIVE: Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decisi...

Integrating machine learning-predicted circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in metastatic breast cancer: A proof of principle study on endocrine resistance profiling.

Cancer letters
The study explored endocrine resistance by leveraging machine learning to establish the prognostic stratification of predicted Circulating tumor cells (CTCs), assessing its integration with circulating tumor DNA (ctDNA) features and contextually eval...

Enhanced breast mass segmentation in mammograms using a hybrid transformer UNet model.

Computers in biology and medicine
Breast mass segmentation plays a crucial role in early breast cancer detection and diagnosis, and while Convolutional Neural Networks (CNN) have been widely used for this task, their reliance on local receptive fields limits ability to capture long-r...

XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.

Scientific reports
Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women's health. The present study outlines a novel approach for analyzing breast cancer data by using the CatBoost classification model with a multi-l...

Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.

Frontiers in immunology
BACKGROUND: Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and person...

Prior information guided deep-learning model for tumor bed segmentation in breast cancer radiotherapy.

BMC medical imaging
BACKGROUND AND PURPOSE: Tumor bed (TB) is the residual cavity of resected tumor after surgery. Delineating TB from CT is crucial in generating clinical target volume for radiotherapy. Due to multiple surgical effects and low image contrast, segmentin...

Technical feasibility of automated blur detection in digital mammography using convolutional neural network.

European radiology experimental
BACKGROUND: The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mam...

Multi-classification of breast cancer pathology images based on a two-stage hybrid network.

Journal of cancer research and clinical oncology
BACKGROUND AND OBJECTIVE: In current clinical medicine, pathological image diagnosis is the gold standard for cancer diagnosis. After pathologists determine whether breast lesions are malignant or benign, further sub-type classification is often nece...

Multi-Institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB.

Journal of the American College of Radiology : JACR
OBJECTIVE: To demonstrate and test the capabilities of the ACR Connect and AI-LAB software platform by implementing multi-institutional artificial intelligence (AI) training and validation for breast density classification.

Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images.

Scientific reports
Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and gradually exceeds the entire body; this kind of cancer originates in both sexes. Prompt recognition of this disorder is most significant in this phase, and it...