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1. chinaXiv:202007.00047 [pdf]


张煦尧; 刘成林
Subjects: Computer Science >> Other Disciplines of Computer Science


submitted time 2020-07-29 Hits4268Downloads624 Comment 0

2. chinaXiv:202006.00176 [pdf]

Automated Radiological Impression Generation for Plain Chest X-rays with End to End Deep Learning

Zhang, Shuai; Xin, Xiaoyan; Shen, Jingtao; Guo, Yachong; Wang, Yang; Yang, Xianfeng; Wang, Jun; Zhang, Jian; Zhang, Bing
Subjects: Computer Science >> Other Disciplines of Computer Science

The chest X-Ray (CXR) is the one of the most common clinical exam used to diagnose thoracic diseases and abnormalities. The volume of CXR scans generated daily in hospitals is huge. Therefore, an automated diagnosis system that is able to save the effort of doctors is of great value. At present, the applications of artificial intelligence in CXR diagnosis usually use pattern recognition to classify the scans. However, such methods rely on labeled databases. They are costly and usually have a high error rate. In this work, we built a database containing more than 12,000 CXR scans and radiological reports, and developed a model based on deep convolutional neural network and recurrent network with attention mechanism. The model learns features from the CXR scans and the associated raw radiological reports directly; no additional labeling required. The model provides automated recognition of given scans and generation of impression. The quality of the generated impression was evaluated with both the CIDEr scores and by radiologists as well. The CIDEr scores were found to be around 5.8 on average for the testing dataset. Further blind evaluation suggested a comparable performance against radiologists.

submitted time 2020-06-09 Hits7372Downloads405 Comment 0

3. chinaXiv:202004.00006 [pdf]


张锦; 舒炫煜; 黄昭彦; 易胜
Subjects: Computer Science >> Other Disciplines of Computer Science


submitted time 2020-03-29 Hits10498Downloads976 Comment 0

4. chinaXiv:202003.00048 [pdf]


张雨; 遆晓光; 张斌; 王春晖
Subjects: Computer Science >> Other Disciplines of Computer Science


submitted time 2020-03-06 Hits17068Downloads865 Comment 1

5. chinaXiv:201903.00220 [pdf]

A method on selecting reliable samples based on fuzziness in positive and unlabeled learning.pdf

TingTing Li; WeiYa Fan; YunSong Luo
Subjects: Computer Science >> Other Disciplines of Computer Science

Traditional semi-supervised learning uses only labeled instances to train a classifier and then this classifier is utilized to classify unlabeled instances, while sometimes there are only positive instances which are elements of the target concept are available in the labeled set. Our research in this paper the design of learning algorithms from positive and unlabeled instances only. Among all the semi-supervised positive and unlabeled learning methods, it is a fundamental step to extract useful information from unlabeled instances. In this paper, we design a novel framework to take advantage of valid information in unlabeled instances. In essence, this framework mainly includes that (1) selects reliable negative instances through the fuzziness of the instances; (2) chooses new positive instances based on the fuzziness of the instances to expand the initial positive set, and we named these new instances as reliable positive instances; (3) uses data editing technique to filter out noise points with high fuzziness. The effectiveness of the presented algorithm is verified by comparative experiments on UCI dataset.

submitted time 2019-03-17 Hits9981Downloads558 Comment 0

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