Segmentation Guided Scoring of Pathological Lesions in Swine Through CNNs

The slaughterhouse is widely recognised as a useful checkpoint for assessing the health status of livestock. At the moment, this is implemented through the application of scoring systems by human experts. The automation of this process would be extremely helpful for veterinarians to enable a systematic examination of all slaughtered livestock, positively influencing herd management. However, such systems are not yet available, mainly because of a critical lack of annotated data. In this work we: (i) introduce a large scale dataset to enable the development and benchmarking of these systems, featuring more than 4000 high-resolution swine carcass images annotated by domain experts with pixel-level segmentation; (ii) exploit part of this annotation to train a deep learning model in the task of pleural lesion scoring. In this setting, we propose a segmentation-guided framework which stacks together a fully convolutional neural network performing semantic segmentation with a rule-based classifier integrating a-priori veterinary knowledge in the process. Thorough experimental analysis against state-of-the-art baselines proves our method to be superior both in terms of accuracy and in terms of model interpretability. Code and dataset are publicly available here: https://github.com/lucabergamini/swine-lesion-scoring.