Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010

One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems

Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods

Asymmetric metabolism by sibling lymphocytes coupling differentiation and self-renewal

After naïve lymphocytes are activated by foreign antigens, they yield cellular progeny with diverse functions, including memory cells, effector cells, and precursors of germinal center B cells. However, it remains unclear whether a naïve lymphocyte is capable of generating daughter cells with multiple fates or multiple naive cells are activated and each give rise to daughter cells with different cell fates. This dissertation analyzes the role of asymmetric cell division in the generation of effector lymphocytes and maintenance of progenitor cells. Our data provide evidence that daughter cells exhibit differential mitochondrial stasis and inherit different amounts of glucose transporters, which is coupled to distinct metabolic and transcriptional program in the sibling cells. To uncover the links between mitochondrial stasis, transcription network reprogramming and cell fate, we perturbed mitochondrial clearance with pharmacological and genetic approaches. I found that the treatments, which impaired mitochondrial function, increased the differentiation of B cells and T cells into effector subsets. Thus, we hypothesize that mitochondrial stasis could be a trigger for effector cell differentiation. To further explore the mechanism for aged mitochondria-induced shifts in transcriptional and metabolic programs, we used reactive oxygen species (ROS) scavengers and glycolysis inhibitors to demonstrate that mitochondria function and the expressions of lineage-specific transcription factors crosstalk through ROS-mediated signaling and activating AMPK. ROS scavenger treatments helped to maintain the progenitor population and suppressed the differentiation of effector subsets, whereas effector cell differentiation was boosted in the AMPK-α1 knockout. These results suggest mitochondrial stress-induced ROS is required for repressing Pax5 and increasing IRF4. In addition to showing mitochondrial stasis’ connection to cell fate, this dissertation also demonstrates the linkage between phosphatidylinositol-3-kinases and glucose transporter 1 (Glut1) in establishing polarity in dividing cells and in transcriptional reprogramming. In sum, this dissertation suggests that asymmetric mitochondrial stasis and nutrient up-take could be part of the driving force of cell fate owing to self-reinforcement and reciprocal inhibition between anabolism and catabolism. These results shed light on the deterministic mechanism of effector cell differentiation and provide clues to the basis of maintenance of self-renewal by activated lymphocytes. These findings could be beneficial for producing memory cells and preventing effector cell exhaustion phenotype in a chronic infection or in cancer microenvironment

Alignment-Free and High-Frequency Compensation in Face Hallucination

Face hallucination is one of learning-based super resolution techniques, which is focused on resolution enhancement of facial images. Though face hallucination is a powerful and useful technique, some detailed high-frequency components cannot be recovered. It also needs accurate alignment between training samples. In this paper, we propose a high-frequency compensation framework based on residual images for face hallucination method in order to improve the reconstruction performance. The basic idea of proposed framework is to reconstruct or estimate a residual image, which can be used to compensate the high-frequency components of the reconstructed high-resolution image. Three approaches based on our proposed framework are proposed. We also propose a patch-based alignment-free face hallucination. In the patch-based face hallucination, we first segment facial images into overlapping patches and construct training patch pairs. For an input low-resolution (LR) image, the overlapping patches are also used to obtain the corresponding high-resolution (HR) patches by face hallucination. The whole HR image can then be reconstructed by combining all of the HR patches. Experimental results show that the high-resolution images obtained using our proposed approaches can improve the quality of those obtained by conventional face hallucination method even if the training data set is unaligned

Depth, balancing, and limits of the Elo model

-Much work has been devoted to the computational complexity of games. However, they are not necessarily relevant for estimating the complexity in human terms. Therefore, human-centered measures have been proposed, e.g. the depth. This paper discusses the depth of various games, extends it to a continuous measure. We provide new depth results and present tool (given-first-move, pie rule, size extension) for increasing it. We also use these measures for analyzing games and opening moves in Y, NoGo, Killall Go, and the effect of pie rules