With the rapid development of AI techniques, Computer-aided Diagnosis has attracted much attention and has been successfully deployed in many applications of health care and medical diagnosis. For some specific tasks, the learning-based system can compare with or even outperform human experts' performance. The impressive performance owes to the excellent expressiveness and scalability of the neural networks, although the models' intuition usually can not be represented explicitly. Interpretability is, however, very important, even the same as the diagnosis precision, for computer-aided diagnosis. To fill this gap, our approach is intuitive to detect pneumonia interpretably. We first build a large dataset of community-acquired pneumonia consisting of 35389 cases (distinguished from nosocomial pneumonia) based on actual medical records. Second, we train a prediction model with the chest X-ray images in our dataset, capable of precisely detecting pneumonia. Third, we propose an intuitive approach to combine neural networks with an explainable model such as the Bayesian Network. The experiment result shows that our proposal further improves the performance by using multi-source data and provides intuitive explanations for the diagnosis results.INDEX TERMS Pneumonia, Computer-aided diagnosis, medical image analysis, interpretive medicalassisted diagnosis, large-scale annotated X-ray image dataset.
AI benchmarking becomes an increasingly important task. As suggested by many researchers, Intelligence Quotient (IQ) tests, which is widely regarded as one of the predominant benchmarks for measuring human intelligence, raises an interesting challenge for AI systems. For better solving IQ tests automatedly by machines, one needs to use, combine and advance many areas in AI including knowledge representation and reasoning, machine learning, natural language processing and image understanding. Also, automated IQ tests provides an ideal testbed for integrating symbolic and sub-symbolic approaches as both are found useful here. Hence, we argue that IQ tests, although not suitable for testing machine intelligence, provides an excellent benchmark for the current development of AI research. Nevertheless, most existing IQ test datasets are not comprehensive enough for this purpose. As a result, the conclusions obtained are not representative. To address this issue, we create IQ10k, a large-scale dataset that contains more than 10,000 IQ test questions. We also conduct a comparison study on IQ10k with a number of state-of-the-art approaches.
An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed. To improve the segmentation accuracy, the developed algorithm combines the FCN semantic segmentation results with the superpixel information acquired by BSLIC. During the combination process, the superpixel semantic annotation is newly introduced and realized by the four criteria. The four criteria are used to annotate a superpixel region, according to FCN semantic segmentation result. The developed algorithm can not only accurately identify the semantic information of the target in the image, but also achieve a high accuracy in the positioning of small edges. The effectiveness of our algorithm is evaluated on the dataset PASCAL VOC 2012. Experimental results show that the developed algorithm improved the target segmentation accuracy in comparison with the traditional FCN model. With the BSLIC superpixel information that is involved, the proposed algorithm can get 3.86%, 1.41%, and 1.28% improvement in pixel accuracy (PA) over respectively.
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