Whole Slide Images (WSIs) are usually gigapixel in size and lack pixel-level annotations. The WSI datasets are also imbalanced in categories. These unique characteristics, significantly different from the ones in natural images, pose the challenge of classifying WSI images as a kind of weakly supervise learning problems. In this study, we propose, RankMix, a data augmentation method of mixing ranked features in a pair of WSIs. RankMix introduces the concepts of pseudo labeling and ranking in order to extract key WSI regions in contributing to the WSI classification task. A two-stage training is further proposed to boost stable training and model performance. To our knowledge, the study of weakly supervised learning from the perspective of data augmentation to deal with the WSI classification problem that suffers from lack of training data and imbalance of categories is relatively un-explored.

With the advent of the artificial intelligence (AI) era, the combination of AI with financial technology (FinTech) has become a development trend in the financial industry. However, deep learning (DL) on the application of automated financial management has been rarely investigated. Thus, this research focuses on the applications of FinTech and DL in asset allocation and aims to optimize investment portfolio. The best investment portfolio in index-based funds based on Taiwan's index-type security investment trust funds are the main investment targets. Time series models for DL, that is, long short-term memory, predict the increase of each investment target and find the best investment portfolio in combination with the relevant asset allocation theory. In this research, we use the Markowitz mean-variance and Black-Litterman models as our asset allocation models for robo-advisor. Results show that the Black-Litterman model has a better accumulated return performance than the Morkowitz model and outperforms other strategies. The Human-Computer Interaction (HCI) dialogue service adopts artificial intelligence markup language (AIML) and a generative model. The main contribution of this paper is that we have developed an integrated knowledge-based and generative-based models for AI conversational robo-advisor.