Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Hierarchical Vector Quantization Scheme

Abstract: Computer-aided detection (CADe) of pulmonary nodules is critical to assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. This paper proposes a novel CADe system based on a hierarchical vector quantization (VQ) scheme. Compared with the commonly-used simple thresholding approach, the high-level VQ yields a more accurate segmentation of the lungs from the chest volume. In identifying initial nodule candidates (INCs) within the lungs, the low-level VQ proves to be effective for INCs detection and segmentation, as well as computationally efficient compared to existing approaches. False-positive (FP) reduction is conducted via rule-based filtering operations in combination with a feature-based support vector machine classifier. The proposed system was validated on 205 patient cases from the publically available online Lung Image Database Consortium database, with each case having at least one juxta-pleural nodule annotation. Experimental results demonstrated that our CADe system obtained an overall sensitivity of 82.7% at a specificity of 4 FPs/scan. Especially for the performance on juxta-pleural nodules, we observed 89.2% sensitivity at 4.14 FPs/scan. With respect to comparable CADe systems, the proposed system shows outperformance and demonstrates its potential for fast and adaptive detection of pulmonary nodules via CT imaging.

Understanding Subtitles by Character-Level Sequence-to-Sequence Learning

Abstract: This paper presents a character-level seque-nce-to-sequence learning method, RNNembed. This method allows the system to read raw characters, instead of words generated by preprocessing steps, into a pure single neural network model under an end-to-end framework. Specifically, we embed a recurrent neural network into an encoder–decoder framework and generate character-level sequence representation as input. The dimension of input feature space can be significantly reduced as well as avoiding the need to handle unknown or rare words in sequences. In the language model, we improve the basic structure of a gated recurrent unit by adding an output gate, which is used for filtering out unimportant information involved in the attention scheme of the alignment model. Our proposed method was examined in a large-scale dataset on an English-to-Chinese translation task. Experimental results demonstrate that the proposed approach achieves a translation performance comparable, or close, to conventional word-based and phrase-based systems.