Acute myeloid leukemia is a type of malignant blood cell cancer that can affect both children and adults. There are 60,140 people were expected to be diagnosed with Leukemia in 2016, according to the Leukemia and Lymphoma Society. In order to get the most effective treatment, the patient needs early diagnosis. Therefore we need to have a support system of early diagnosis to guide treatment for patients with acute leukemia as soon as possible. In this paper, the authors propose a Convolutional Neural Network (CNN) based method to distinguish normal and abnormal blood cell images. The proposed method achieves an accuracy up to 96.6% with the dataset including 1188 blood cell images.

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Emergence of novel techniques devices e.g., MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach to extract the discriminative patterns for efficient human action recognition. Each action is considered to consist of a sequence of unit actions, each of which is represented by a pattern. Given a skeleton sequence, we first automatically extract the key-frames, and then categorize them into different patterns. We further use a statistical metric to evaluate the discriminative capability of patterns, and define them as local features for action recognition. Experimental results show that the extracted local descriptors could provide very high accuracy in the action recognition, which demonstrate the efficiency of our method in extracting discriminative unit actions.

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With the development of the technology like 3D specialized markers, we could capture the moving signals from marker joints and create a huge set of 3D action MoCap data. The more we understand the human action, the better we could apply it to applications like security, analysis of sports, game etc. In order to find the semantically representative features of human actions, we extract the sets of action characteristics which appear frequently in the database. We then propose an Apriori-like algorithm to automatically extract the common sets shared by different action classes. The extracted representative action characteristics are defined in the semantic level, so that it better describes the intrinsic differences between various actions. In our experiments, we show that the knowledge extracted by this method achieves high accuracy of over 80% in recognizing actions on both training and testing data.