# Deep CNN for IIF Images Classification in Autoimmune Diagnostics
## 1 Introduction  
Antinuclear antibodies (ANAs) are a very large category of autoantibodies, or antibodies that the body produces against itself. They are related to many autoimmune diseases [1]. There are several laboratory investigations for the research and differentiation of antibodies to the nucleus. The most commonly used technique is indirect immunofluorescence, which uses a substrate of HEp-2 cells, that is, human epithelial type 2 cells, to detect antibodies to the nucleus. These particular cells show a very high nucleus/cytoplasm ratio and, by virtue of their neoplastic nature, present numerous mitotic figures allowing the operator to identify antibodies directed against the cellular antigens expressed during the mitotic phase. The ANA research is the first level test in the diagnosis of systemic autoimmune diseases, linked to an altered regulation of immune tolerance control mechanisms and characterized by the production of antibodies directed against cellular components that are no longer recognized as \"self\". Indirect immunofluorescence (IIF) is the reference method for the determination of ANAs. Despite recent progress in the standardization of the indirect immunofluorescence method (automation of the analytical procedure), the technique still presents some methodological and interpretative limitations [2,3]. The importance of autoimmune diseases and the need for their early diagnosis has pushed the interest of manufacturers and the research world to develop expert fluoroscopicclassification systems [4; 5; 6; 7; 8]. Figure 1 shows some examples of fluoroscopic patterns. As evinced in the figure, some patterns could be easily misidentified (i.e., homogeneous vs speckled). In [PERSON] et al. [9] this difficulty of interpretation has been quantified by finding the percentage of concordance between the classifications of two senior immunologists; the analysis on 589 wells, both positive (six types of patterns) and negative, showed a concordance of around 71%. Automatic systems to support diagnosis and in particular computer-aided diagnosis (CAD) systems are widely used for different tasks within medicine such as second reading, increasing the diagnosis speed, training physicians for special task, etc. [10; 11]. In the last few years, the interest of the scientific community in the problem of HEp-2 cells classification or staining pattern recognition has been remarkable. This was also due to various contests that were organized on the topic [12; 13; 14], and to the availability of the first public databases [15; 16]. In the work of [PERSON] et al. [17] the authors extracted sets of local features that were aggregated through sparse encoding. They used a pyramidal decomposition of the cell that consisted in the central part and in the crown that contains the cell membrane. Linear support vector machines (SVMs) are the classifiers used on the learned dictionary; specifically, they used four SVMs, the first trained on the orientation of the original images and the remaining three on the images rotated 90, 180 and 270 degrees, respectively. With this technique they won the indirect immunofluorescence images analysis (I3A) competition in the International Conference on Pattern Recognition (ICPR) 2014. [PERSON] et al. [18] developed a multiscale method using shape index histograms. The decomposition was spatial and presented a radially symmetric pattern. Second-order descriptors of textural type were extracted from the decompositions using a shape index. Finally, the shape index statistics were collected in histograms. The system was trained and tested on data from the I3A Task-1 public database. [PERSON] et al. [19] used a variant of the superpixel approach to extract patches from cells in combination with the sparse codes scheme. The features were extracted from both the superpixel patches and their boundary (high gradient zones). From the patches, features we extracted using scale-invariant feature transform (SIFT) and speeded up robust feature (SURF) to form a dictionary learning.