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Player behaviors can have a significant impact on the outcome of individual events, as well as the game itself. The increased availability of high quality resolution spatio-temporal data has enabled analysis of player behavior and game strategy. In this paper, we present the implementation and evaluation of an imitation learning method using recurrent neural networks, which allows us to learn individual player behaviors and perform rollouts of player movements on previously unseen play sequences. The method is evaluated using a 2019 dataset from the top-tier soccer league in Sweden (Allsvenskan). Our evaluation provides insights how to best apply the method on movement traces in soccer, the relative accuracy of the method, and how well policies of one player role capture the relative behaviors of a different player role, for example.

Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Central but very few models can use such datasets because of the variability of the data in color and scale and a lack of metadata. In this article, we present and compare two deep learning architectures, to detect the scale of histopathology image patches. The approach is evaluated on a patch dataset from whole slide images of the prostate, obtaining a Cohen's kappa coefficient of 0.9897 in the classification of patches with a scale of 5×, 10× and 20×. The good results represent a first step towards magnification detection in histopathology images that can help to solve the problem on more heterogeneous data sources.

Grading whole slide images (WSIs) from patient tissue samples is an important task in digital pathology, particularly for diagnosis and treatment planning. However, this visual inspection task, performed by pathologists, is inherently subjective and has limited reproducibility. Moreover, grading of WSIs is time consuming and expensive. Designing a robust and automatic solution for quantitative decision support can improve the objectivity and reproducibility of this task. This paper presents a fully automatic pipeline for tumor proliferation assessment based on mitosis counting. The approach consists of three steps: i) region of interest selection based on tumor color characteristics, ii) mitosis counting using a deep network based detector, and iii) grade prediction from ROI mitosis counts. The full strategy was submitted and evaluated during the Tumor Proliferation Assessment Challenge (TUPAC) 2016. TUPAC is the first digital pathology challenge grading whole slide images, thus mimicking more closely a real case scenario. The pipeline is extremely fast and obtained the 2nd place for the tumor proliferation assessment task and the 3rd place in the mitosis counting task, among 17 participants. The performance of this fully automatic method is similar to the performance of pathologists and this shows the high quality of automatic solutions for decision support.