AIMC Topic: Mammography

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Dual-Modality Virtual Biopsy System Integrating MRI and MG for Noninvasive Predicting HER2 Status in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is critical for guiding targeted therapy in breast cancer. This study aimed to develop and validate a deep learning (DL)-based decision-mak...

Economics of AI and human task sharing for decision making in screening mammography.

Nature communications
The rising global incidence of breast cancer and the persistent shortage of specialized radiologists have heightened the demand for innovative solutions in mammography screening. Artificial intelligence (AI) has emerged as a promising tool to bridge ...

Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study.

Nature communications
Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-base...

Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.

Scientific reports
Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current dia...

Deep learning-driven prediction in healthcare systems: Applying advanced CNNs for enhanced breast cancer detection.

Computers in biology and medicine
The mortality risk associated with breast cancer is experiencing an exponential rise, underscoring the critical importance of early detection. It is the primary cause of mortality among women under 50 and ranks as the second deadliest disease globall...

An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features.

Scientific reports
Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct a clean, noise-free image from a noisy input, such as a mammogram image. In recent years, deep learning, particularly convolutional neural networks (C...

Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction.

Physics in medicine and biology
State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the model's BCR discriminatio...

Deep transfer learning based hierarchical CAD system designs for SFM images.

Journal of medical engineering & technology
Present work involves rigorous experimentation for classification of mammographic masses by employing four deep transfer learning models using hierarchical framework. Experimental work is carried on 518 SFM images of DDSM dataset with 208, 150 and 16...

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Scientific reports
Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strateg...

[Artificial intelligence in breast imaging : Hopes and challenges].

Radiologie (Heidelberg, Germany)
CLINICAL/METHODICAL ISSUE: Artificial intelligence (AI) is being increasingly integrated into clinical practice. However, the specific benefits are still unclear to many users.