AIMC Topic: Breast Neoplasms
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Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.
BMC medical imaging
Jul 1, 2025
OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.
Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images.
Scientific reports
Jul 1, 2025
Breast cancer is the second leading cause of cancer-related deaths among women, following lung cancer, as of 2024. Conventional cancer diagnosis relies on the manual examination of biopsied tissues by pathologists, a time-consuming process that may v...
Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques.
Scientific reports
Jul 1, 2025
Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical...
An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model.
Scientific reports
Jul 1, 2025
The expansion rate of medical data during the past ten years has rapidly expanded due to the vast fields. The automated disease diagnosis system is proposed using a deep learning (DL) algorithm, which automates and helps speed up the process efficien...
Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps.
Scientific reports
Jul 1, 2025
This study integrates multimodal metabolomic data from three platforms-LC-MS, GC-MS, and NMR-to systematically identify biomarkers distinguishing breast cancer subtypes. A feedforward attention-based deep learning model effectively selected 99 signif...
An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.
Scientific reports
Jul 1, 2025
Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. The manual classification of these is a hectic and time-consuming pr...
Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.
Nature communications
Jul 1, 2025
Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients w...
3Mont: A multi-omics integrative tool for breast cancer subtype stratification.
PloS one
Jun 27, 2025
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types,...
Multi-site validation of an interpretable model to analyze breast masses.
PloS one
Jun 26, 2025
An external validation of IAIA-BL-a deep-learning based, inherently interpretable breast lesion malignancy prediction model-was performed on two patient populations: 207 women ages 31 to 96, (425 mammograms) from iCAD, and 58 women (104 mammograms) f...
Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.
Biology direct
Jun 23, 2025
BACKGROUND: Pathological complete response (pCR) to neoadjuvant systemic therapy (NAST) is an established prognostic marker in breast cancer (BC). Multimodal deep learning (DL), integrating diverse data sources (radiology, pathology, omics, clinical)...
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