Automated Sperm Assessment Framework and Neural Network Specialized for Sperm Video Recognition

Infertility is a global health problem, and an increasing number of couples are seeking medical assistance to achieve reproduction, at least half of which are caused by men. The success rate of assisted reproductive technologies depends on sperm assessment, in which experts determine whether sperm can be used for reproduction based on morphology and motility of sperm. Previous sperm assessment studies with deep learning have used datasets comprising images that include only sperm heads, which cannot consider motility and other morphologies of sperm. Furthermore, the labels of the dataset are one-hot, which provides insufficient support for experts, because assessment results are inconsistent between experts, and they have no absolute answer. Therefore, we constructed the video dataset for sperm assessment whose videos include sperm head as well as neck and tail, and its labels were annotated with soft-label. Furthermore, we proposed the sperm assessment framework and the neural network, RoSTFine, for sperm video recognition. Experimental results showed that RoSTFine could improve the sperm assessment performances compared to existing video recognition models and focus strongly on important sperm parts (i.e., head and neck).Comment: Accepted at Winter Conference on Applications of Computer Vision (WACV) 202

Theoretical adaptation of multiple rule-generation in XCS

Most versions of the XCS Classifier System have been designed to evolve only two rules for each rule discovery invocation, which restricts the search capacity. A difficulty behind generating multiple rules each time is the increase in the probability of deleting immature rules, which conflicts with the requirement that parent rules be sufficiently updated so that fitness represents worth. Thus the aim of this paper is to argue how XCS determines when rules can be deleted safely. The objectives are to certainly identify inaccurate rules and then to maximize how many rules XCS can generate. The proposed method enables adaptation of rule-generation that maximizes the number of generated rules, under the assumption that the reliably inaccurate rules can be replaced with new rules. Experiments show our modification strongly improves the XCS performance on large scale problems, since it can take advantage of multi-point search more efficiently. For example, on the 135-bit multiplexer problem, XCS with our modification requires 1.57 million less training inputs compared with the standard XCS while utilizing the same number of final rules.</p

Construction of visual codebook to speed up visual-based simultaneous localization and mapping

We propose a visual-based self-localization system that requires less computational time and memory resources when utilized in an indoor environment. In this study, localization is accomplished by comparing an observation with a significant amount of preprocessed images. The larger the database, the slower the self-location calculation. We constructed an economical database (codebook for local image features), in terms of computational time and memory usage, to resolve this challenge. We sequentially registered new codes to avoid duplication. Additionally, we indexed the codebook to speed up the calculation when using an approximating search. The experiment result illustrated that the strategies of reducing the size of the codebook and approximating the calculation contributed to reducing the calculation cost and improving the self-localization accuracy