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In general image recognition problems, discriminative information often lies in local image patches. For example, most human identity information exists in the image patches containing human faces. The same situation stays in medical images as well. "Bodypart identity" of a transversal slice-which bodypart the slice comes from-is often indicated by local image information, e.g., a cardiac slice and an aorta arch slice are only differentiated by the mediastinum region. In this work, we design a multi-stage deep learning framework for image classification and apply it on bodypart recognition. Specifically, the proposed framework aims at: 1) discover the local regions that are discriminative and non-informative to the image classification problem, and 2) learn a image-level classifier based on these local regions. We achieve these two tasks by the two stages of learning scheme, respectively. In the pre-train stage, a convolutional neural network (CNN) is learned in a multi-instance learning fashion to extract the most discriminative and and non-informative local patches from the training slices. In the boosting stage, the pre-learned CNN is further boosted by these local patches for image classification. The CNN learned by exploiting the discriminative local appearances becomes more accurate than those learned from global image context. The key hallmark of our method is that it automatically discovers the discriminative and non-informative local patches through multi-instance deep learning. Thus, no manual annotation is required. Our method is validated on a synthetic dataset and a large scale CT dataset. It achieves better performances than state-of-the-art approaches, including the standard deep CNN.

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Researchers in the fields of computer graphics and geographical information systems (GISs) have extensively studied the methods of extracting terrain features such as peaks, pits, passes, ridges, and ravines from discrete elevation data. The existing techniques, however, do not guarantee the topological integrity of the extracted features because of their heuristic operations, which results in spurious features. Furthermore, there have been no algorithms for constructing topological graphs such as the surface network and the Reeb graph from the extracted peaks, pits, and passes. This paper presents new algorithms for extracting features and constructing the topological graphs using the features. Our algorithms enable us to extract correct terrain features; i.e., our method extracts the critical points that satisfy the Euler formula, which represents the topological invariant of smooth surfaces. This paper also provides an algorithm that converts the surface network to the Reeb graph for representing contour changes with respect to the height. The discrete elevation data used in this paper is a set of sample points on a terrain surface. Examples are presented to show that the algorithms also appeal to our visual cognition.