相关论文: OPTIMAM Mammography Image Database: a large scale …

The past decade has witnessed a substantial increase in the number of startups and companies offering AI-based solutions for clinical decision support in medical institutions. However, the critical nature of medical decision-making raises…

Early detection of breast cancer is critical for improving patient outcomes. While mammography remains the primary screening modality, magnetic resonance imaging (MRI) is increasingly recommended as a supplemental tool for women with dense…

Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of…

In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer.…

Breast cancer as a medical condition and mammograms as images exhibit many dimensions of variability across the population. Similarly, the way diagnostic systems are used and maintained by clinicians varies between imaging centres and…

Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse. To date, the majority of publicly available breast imaging datasets lack in one or more of these areas.…

Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer screening. We developed a multi-modal artificial intelligence…

A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are often only trained on data from advantaged populations that…

Background. Breast cancer screening programs using mammography have led to significant mortality reduction in high-income countries. However, many low- and middle-income countries lack resources for mammographic screening. Handheld breast…

Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest and most diverse dataset to date - 140,677 patients…

The development of clinically reliable artificial intelligence (AI) systems for mammography is hindered by profound heterogeneity in data quality, metadata standards, and population distributions across public datasets. This heterogeneity…

The aim was to undertake a national survey of the setup of mammography imaging systems in the UK, we were particularly interested in image processing and software version. We created a program that can extract selected tags from the DICOM…

Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival clinical experts in a variety of scenarios. The National…

Risk-adapted breast cancer screening requires robust models that leverage longitudinal imaging data. Most current deep learning models use single or limited prior mammograms and lack adaptation for real-world settings marked by imbalanced…

The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning…

OncoVision is a multimodal AI pipeline that combines mammography images and clinical data for better breast cancer diagnosis. Employing an attention-based encoder-decoder backbone, it jointly segments four ROIs - masses, calcifications,…

Mammographic breast density is a well-established risk factor for breast cancer. Recently there has been interest in breast MRI as an adjunct to mammography, as this modality provides an orthogonal and highly quantitative assessment of…

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted…

Precision medicine has become a central focus in breast cancer management, advancing beyond conventional methods to deliver more precise and individualized therapies. Traditionally, histopathology images have been used primarily for…

An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection. With an increased focus on technologies to aid healthcare, Mammogram images have been…