A primer on deep learning and convolutional neural networks for clinicians

Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. Key Points: Citation Format

Imaging Techniques for Breast Cancer Diagnosis

Breast cancer is the most typical variety of cancer in women worldwide. Mammography is the “gold standard” for the analysis of the breast from an imaging perspective. Altogether, the techniques used within the management of cancer in all stages are multiple biomedical imaging. Imaging as a very important part of cancer clinical protocols can offer a range of knowledge regarding morphology, structure, metabolism, and functions. Supported by relevant literature, this text provides an outline of the previous and new modalities employed in the sector of breast imaging. Any progress in technology can result in increased imaging speed to satisfy physiological processes necessities. One of the problems within the designation of breast cancer is sensitivity limitation. To overcome this limitation, complementary imaging examinations are used that historically include screening, ultrasound, MRI, etc.