Image-based second opinion for blood typing

This paper considers a new method for providing a recommendation (second opinion) for a laboratory assistant in manual blood typing based on serological plates. The manual method consists of two steps: preparation and analysis. During preparation step the laboratory assistant needs to fill each well of a plate with a blood sample and a reagent mixture according to methodological guidelines. In the second step it is necessary to visually determine the result of the reactions, named agglutination. Despite the popularity of this method, it is slow and highly influenced by human factor, which cause blood typing errors. To increase the quality and performance of the analysis step, we propose a novel neural-based classification method. Our solution provides a fast way to fill the results into a laboratory system. We collected a new large dataset consisting of 3139 well images with GTs from donors' medical history and six experts' assessment for each. We showed that the proposed solution based on state-of-the-art architectures is comparable with the best expert and has 2.75 times fewer errors than the average one, with an overall accuracy equal to 98.4%. Taking into account the low-semantic nature of the task, we also considered shallow neural networks, which showed accuracy comparable with state-of-the-art models.