# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 5 Description of AI Toolbox/Breast Cancer Tools
### Preliminary Results--Initial Analysis on Mammography
#### 5.2.1 Data Quality and Related Pre-Processing  
INCISIVE bases its AI development on both open datasets and collected data. In order to achieve good generalization of AI models, certain robustness has to be ensured with respect to input image quality. For these reasons, the heterogeneity of collected MG images in all relevant parameters has to be investigated. The guidelines for quality assurance in breast cancer screening [62] set the clear minimum rules on how MG imaging is to be performed, covering all physical aspects of the imaging process, with the calibration, radiation dose, and exposure time being the main determinants of the MG image quality [63]. Once an MG image is produced, the image quality can be improved by relying on digital image processing tools, focusing on contrast enhancement and noise removal [62].  
When creating the INCISIVE database, MG images could be uploaded as raw images, i.e., produced by the detector, corrected for gain and offset, and processed where contrast enhancement and noise removal have already been applied by manufacturer software. In the data collection process, image quality has not been automatically checked, but the correspondence between clinical metadata and imaging data tested in each time point. For these reasons, all MG images in the repository had to be automatically assessed for quality to ensure their usability in AI models and to set some additional rules for the data donorship schema.  
Guided by the experiences in collecting the largest MG image database so far (NYUBCS) [57], the DICOM header of MG images had to be inspected to remove all images that did not relate to the MLO or CC view of the left or right breast. It should be noted that different types of errors during the examination are possible, yet these images are not usually deleted by the hospital technicians and can be accidentally uploaded. When filtering images, there are multiple possible reasons to discard an image, such as poor image resolution, views other than CC or MLO, a lack of important information in the DICOM header in relation to the SOP, image type, if an image is scanned for surgical guidance, and if the proportion of nonzero pixels is more than 95% or less than 5%.  
In order to provide uniform image appearance at the model input, multiple contrast enhancement pre-processing methods have to be evaluated jointly with AI model training in order to select the most appropriate procedure for image pre-processing. For every image, information on the basic image attributes will be derived from its DICOM header (or alternatively automatically if certain DICOM fields are missing), and preprocessing will be dependent on the image attributes. Currently, in the INCISIVE database, all MG images uploaded are denoted as processed (for presentation); however, despite this field, they do not have uniform appearance and might even be presented as negative. Figure 12 provides some examples of INCISIVE MG images from different data providers. Figure 13a shows an MG image with an automatically identified breast region (ROI), while panel (b) presents the image histogram (in blue), and a proposed piecewise linear intensity transformation in red. The enhanced ROI is presented in Figure 13d with a histogram indicating an improved utilization of the available intensity range (Figure 13c). An automated procedure estimating the dynamic range of an image, as well as the contrast and noise levels, will determine the transformation needed to harmonize the image appearance.