Periodic Reporting for period 2 - SmartMammaCAD (Intelligent Automated System for detecting Diagnostically Challenging Breast Cancers)
Reporting period: 2017-09-01 to 2018-08-31
Now days, computers have dramatically increased their computation abilities. With the rise of artificial intelligence, high demanding tasks can be achieved in a matter of seconds with the aid of new machine learning algorithms. Also, the field of medical imaging has notably contributed to assist physicians with novel and non-invasive techniques for diagnosis and prognosis, through high resolution images of the internal organs by magnetic resonance imaging (MRI), or nuclear imaging. The main goal and overall objective of this project is to develop computer aided diagnosis (CAD) methods, and image processing techniques to improve diagnostic accuracy and efficiency of cancer-related breast lesions. CAD systems will aid physicians by detecting challenging lesions with high accuracy, ""learning"" to detect them from previously diagnosed cases. Those challenging cases are usually non-mass enhancing lesions, not easy to detect by direct methods, or requiring invasive methods to diagnose them, such as biopsies. Therefore, the use of CAD systems in the clinical practice is not only important for reducing the impact of the most aggressive breast cancers, but also to increase the patient care and comfort.
With the development of this project, several objectives have been achieved such as: analysis of registration algorithms, development of whole breast segmentation algorithms and feature extraction and classification techniques for NME breast lesion detection and classification. The developed analyses and techinques have been shown to increase the performance of CAD systems for NME breast lesion detection and classification, by solving some of the problems involved in detection and classification, including a methodology for false positive control."
Moreover, blind source separation techinques have been proved to be usefull in the analysis and identification of relevant kinetic features for malignant tissues classification. This prove-of-concept study will be trained and validated on a big dataset of clinical practice cases, with an open source implementation for clinical use, a pilot tool developed in collaboration with radiologists. This tool will be optimized for detecting non-mass enhancing lessions that usually require biopsies for diagnosis, and have been proven to provide a false positive rate control, improving the CAD performance and potentially increasing the radiologist confidence in the CAD system. By augmenting radiologists diagnostic capabilities through CAD systems,