Abstract: A system and method is disclosed to reduce variation of the clinical decision making process when an image reporting and data system (IRADS) for medical diagnosis is used. Image reporting and data systems provide guidelines for an operator to identify images as belonging to one of a number of categories and specific clinical actions are then recommended based upon such categories. Some clinical actions such as biopsies may be recommended by IRADS even when they are not necessary. The present inventive concept is configured to utilize a Computer-Assisted Diagnosis (CAD) system that is specifically programmed to minimize discrepancies between the recommended clinical actions of an individual or specific group of experts using the standard IRADS process and the optimum clinical actions based on correlation with biopsy proven data. The resulting CAD system reduces the number of unnecessary clinical actions such as biopsies based on the operator's error profile.

Abstract: A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

Abstract: A system and method is disclosed to reduce variation of the clinical decision making process when an image reporting and data system (IRADS) for medical diagnosis is used. Image reporting and data systems provide guidelines for an operator to identify images as belonging to one of a number of categories and specific clinical actions are then recommended based upon such categories. Some clinical actions such as biopsies may be recommended by IRADS even when they are not necessary. The present inventive concept is configured to utilize a Computer-Assisted Diagnosis (CAD) system that is specifically programmed to minimize discrepancies between the recommended clinical actions of an individual or specific group of experts using the standard IRADS process and the optimum clinical actions based on correlation with biopsy proven data. The resulting CAD system reduces the number of unnecessary clinical actions such as biopsies based on the operator's error profile.

Abstract: A method and means to utilize machine learning to train a device to generate a confidence level indicator (CLI). The device is a CAD system that has been initially trained using initial machine learning to recommend classifications for image features presented to the device. Probabilistic classification is utilized to incorporate intermediate values given by a human operator to better indicate a level of confidence of the CAD system's recommendations as to what classes should be associated with certain image features.

Abstract: A system and method of processing an ultrasound signal may include, in response to receiving a set of complex frequency samples of the ultrasound signal inclusive of content data and noise at a first noise level and being used to image an anatomical region of a body, resampling multiple subsets of complex frequency samples from the set of complex frequency samples. The resampled subsets of complex frequency samples may be resampled from a first domain into a second domain. The transformed resampled subsets of complex frequency samples may be combined in the second domain to produce a result signal with a second noise level reduced from the first noise level. An image derived from the result signal may be displayed.