# 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  
Medical images differ to natural images in many aspects, and most importantly by their high resolution and very small regions of interest (ROIs). For these reasons, if deep learning models are trained and developed for natural images [50; 51; 52; 53], they assume a severe down-sampling of medical images for memory constraints and offer too coarse localization [54]. In high-resolution MG images, in early asymptomatic stages relevant for breast cancer screening, ROIs are usually very small, with subtle differences to the normal tissue and often sparsely distributed [54]. In order to deliver an accurate decision, an MG classifier should not only detect suspicious changes (e.g., lesions and calcifications), but also take into consideration their characteristics (e.g., intensity, shape, and position) and some global features (e.g., breast fibro-glandular tissue density and pattern) [55; 56]. Based on the extensive literature review, in the INCISIVE, the focus will be placed on few recently proposed architectures for the segmentation of lesions and malignancy classification which will be trained or evaluated on both internally collected data and available MG open datasets. The relevant line of research is based on the NYU breast cancer screening dataset (NYUBCS) [57] comprising 1 million MG images, which facilitated the development of the classification models being fully trained on MG images. In the INCISIVE, several explainable classification models developed on NYUBSC will be tested. The breast-label classification model, a globally aware multiple instance classifier (GMIC) [58], will serve as a baseline, as it achieves AUC = 0.93 on NYUBSC. As the segmentation of lesions is one of the main tasks in INCISIVE, the concept of weakly supervised localization (WSL) [59] has been explored in order to alleviate the problem of expensive and time-consuming experts' pixel-level annotation. Aligned with WSL, the global-local activation map (GLAM) model [54] uses only a breast-level label to produce a fine segmentation at a 300-times-higher resolution of saliency maps when compared with GMIC [58], and an improvement of 20%, as measured by the DICE index on the NYU BCS MG database. So far, both GMIC and GLAM input pipelines were formed with the specific processing steps described in [54; 58], respectively. Additional steps were made to correct any potential differences of INCISIVE MG images from the training NYUBSC data in terms of contrast and dynamic ranges, while following other noted required attributes from the NYU BCS image selection process. By thresholding the corresponding output saliency maps, specific regions can be segmented and identified as benign or malignant, as determined by the model. Besides these models trained on the closed NYU BCS MG database, and clearly focused on MG classification and WSL, there are some other more general approaches to medical image segmentation, not restrained to MG. In INCISIVE, the medical transformers (MTs) [60] will be trained and evaluated using the INCISIVE data on images with available pixel-level annotation. The pipelines have already been prepared and efforts directed to the harmonization of image attributes from different data providers within INCISIVE. The preprocessed MG images are automatically cropped to ROIs and rescaled to a modest 256 \(\times\) 256 size required at input. nnU-Net is a recently proposed medical image segmentation method which aims to alleviate the need for a highly specialized solution for numerous distinctive problems in medical imaging [61]. It should be automatically configurable for any new task, adjusting all relevant steps: preprocessing, network architecture, training, and post-processing. nnU-net code has been modified to work on single-view MG images involving the conversion of 2D DICOM images into 3D NIFTI images and their organization into an appropriate folder structure. Images were normalized, cropped to ROIs, and rescaled to the same size (320 \(\times\) 416).