This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

This article presents system performance studies of the CareMiBrain dedicated brain PET according to NEMA NU 2-2012 (for whole-body PETs) and NU 4-2008 (for preclinical PETs). This scanner is based on monolithic LYSO crystals coupled to silicon photomultipliers. The results obtained for both protocols are compared with current commercial whole body PETs and dedicated brain PETs found in the literature. Spatial resolution, sensitivity, NECR and scatter-fraction are characterized with NEMA standards, as well as an image quality study. A customized image quality phantom is proposed as NEMA phantoms do not fulfil the necessities of dedicated brain PETs. The full-width half maximum of the radial/tangential/axial spatial resolution of CareMiBrain reconstructed with FBP at 10 and 100 mm from the system center were, respectively, 1.87/1.68/1.39 mm and 1.86/1.91/1.40 mm (NU 2-2012) and 1.58/1.45/1.40 mm and 1.64/1.66/1.44 mm (NU 4-2008). Peak NECR was 49 kcps@287 MBq with a scatter fraction of 48% using NU 2-2012 phantom. The sensitivity was 13.82 cps/kBq at the center of the FOV (NU 2-2012) and 10% (NU 4-2008). Contrast recovery coefficients for customizing image quality phantom were 0.73/0.78/1.14/1.01 for the 4.5/6/9/12 mm diameter rods. The performance characteristics of CareMiBrain are at the top of the current technologies for PET systems. Dedicated brain PET systems significantly improve spatial resolution and sensitivity, but present worse results in count rate measurements and scatter-fraction tests. As for the comparison of preclinical and clinical standards, the results obtained for solid and liquid sources were similar.