Segmentation of diagnostic tissue compartments on whole slide images with renal thrombotic microangiopathies (TMAs)

The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings. Precise diagnostic criteria for a renal biopsy diagnosis of TMA are missing. As a first step towards a machine learning- and computer vision-based analysis of wholes slide images from renal biopsies, we trained a segmentation model for the decisive diagnostic kidney tissue compartments artery, arteriole, glomerulus on a set of whole slide images from renal biopsies with TMAs and Mimickers (distinct diseases with a similar nephropathological appearance as TMA like severe benign nephrosclerosis, various vasculitides, Bevacizumab-plug glomerulopathy, arteriolar light chain deposition disease). Our segmentation model combines a U-Net-based tissue detection with a Shifted windows-transformer architecture to reach excellent segmentation results for even the most severely altered glomeruli, arterioles and arteries, even on unseen staining domains from a different nephropathology lab. With accurate automatic segmentation of the decisive renal biopsy compartments in human renal vasculopathies, we have laid the foundation for large-scale compartment-specific machine learning and computer vision analysis of renal biopsy repositories with TMAs.

Handgun detection using combined human pose and weapon appearance

CCTV surveillance systems are essential nowadays to prevent and mitigate security threats or dangerous situations such as mass shootings or terrorist attacks, in which early detection is crucial. These solutions are manually supervised by a security operator, which has significant limitations. Novel deep learning-based methods have allowed to develop automatic and real time weapon detectors with promising results. However, these approaches are based on visual weapon appearance only and no additional contextual information is exploited. For handguns, body pose may be a useful cue, especially in cases where the gun is barely visible and also as a way to reduce false positives. In this work, a novel method is proposed to combine in a single architecture both weapon appearance and 2D human pose information. First, pose keypoints are estimated to extract hand regions and generate binary pose images, which are the model inputs. Then, each input is processed with a different subnetwork to extract two feature maps. Finally, this information is combined to produce the hand region prediction (handgun vs no-handgun). A new dataset composed of samples collected from different sources has been used to evaluate model performance under different situations. Moreover, the robustness of the model to different brightness and weapon size conditions (simulating conditions in which appearance is degraded by low light and distance to the camera) have also been tested. Results obtained show that the combined model improves overall performance substantially with respect to appearance alone as used by other popular methods such as YOLOv3.

Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues

Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.