In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95. The approach described here has achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are published online at https://github.com/riblidezso/frcnn_cad.

In the absence of intestinal metaplasia H. pylori infection increases both apoptotic activity and expression of p53 oncoprotein in the gastric mucosa. The lack of increased apoptosis with a higher p53 expression in the presence of intestinal metaplasia suggests an increased genetic instability and also may suggest that mutation of the p53 gene is an early step in the multistep process of gastric carcinogenesis.

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Summary. Our aim was to compare the expression of EGFR and proliferative cell nuclear antigen (PCNA) in different histological and endoscopic diagnostic groups, in cases of Helicobacter pylori infection, in vivo. Paraffin embedded human gastric biopsy samples (86) were analysed by EGFR and PCNA immunohistochemistry and classified both on the basis of histology and endoscopic findings. In normal epithelia (NE), a positive correlation was found between PCNA and EGFR and in H. pylori-negative gastritis with and without intestinal metaplasia (P < 0.01). On the other hand, a negative correlation was detected between the two immunohistochemical findings in H. pyloriassociated gastritis with intestinal metaplasia (HPGIM) and in the atrophic gastritis (AG) group. In HPGIM the percentage of EGFR-positive cells was significantly lower (32.4 AE 30.4) when compared to either the NE (50.3 AE 23.7) or H. pylori-negative gastritis with intestinal metaplasia (HNGIM) (48.3 AE 23.7). In AG, EGFR was significantly lower when compared to the NE (P < 0.05). Based on the endoscopic findings, a significant decrease of EGFR expression was found in gastric ulcer cases as compared to NE, gastritis or erosion cases (P < 0.01). PCNA showed no significant alterations between the NE and gastritis, AG groups. The presence of H. pylori has an inverse effect on PCNA and EGFR expression in HPGIM.