BACKGROUND: To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model. RESULTS: The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively. CONCLUSION: The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.

In cloud systems, incidents affect the availability of services and require quick mitigation actions. Once an incident occurs, operators and developers often examine logs to perform fault diagnosis. However, the large volume of diverse logs and the overwhelming details in log data make the manual diagnosis process time-consuming and error-prone. In this paper, we propose Onion, an automatic solution for precisely and efficiently locating incident-indicating logs, which can provide useful clues for diagnosing the incidents. We first point out three criteria for localizing incident-indicating logs, i.e., Consistency, Impact, and Bilateral-Difference. Then we propose a novel agglomeration of logs, called log clique, based on which these criteria are satisfied. To obtain log cliques, we develop an incident-aware log representation and a progressive log clustering technique. Contrast analysis is then performed on the cliques to identify the incident-indicating logs. We have evaluated Onion using well-labeled log datasets. Onion achieves an average F1-score of 0.95 and can process millions of logs in only a few minutes, demonstrating its effectiveness and efficiency. Onion has also been successfully applied to the cloud system of Microsoft. Its practicability has been confirmed through the quantitative and qualitative analysis of the real incident cases.

Abstract The purpose of this study was to establish and validate a new deep learning system that generates quantitative computed tomography (CT) reports for the diagnosis of pulmonary tuberculosis (PTB) in clinic. 501 CT imaging datasets were collected from 223 patients with active PTB, while another 501 datasets, which served as negative samples, were collected from a healthy population. All the PTB datasets were labeled and classified manually by professional radiologists. Then, four state-of-the-art 3D convolution neural network (CNN) models were trained and evaluated in the inspection of PTB CT images. The best model was selected to annotate the spatial location of lesions and classify them into miliary, infiltrative, caseous, tuberculoma, and cavitary types. The Noisy-Or Bayesian function was used to generate an overall infection probability of this case. The results showed that the recall and precision rates of detection, from the perspective of a single lesion region of PTB, were 85.9% and 89.2%, respectively. The overall recall and precision rates of detection, from the perspective of one PTB case, were 98.7% and 93.7%, respectively. Moreover, the precision rate of type classification of the PTB lesion was 90.9%. Finally, a quantitative diagnostic report of PTB was generated including infection possibility, locations of the lesion, as well as the types. This new method might serve as an effective reference for decision making by clinical doctors.

Identifying critical information in real time in the beginning of a disaster is a challenging but important task. This task has been recently addressed using domain adaptation approaches, which eliminate the need for target labeled data, and can thus accelerate the process of identifying useful information. We propose to investigate the effectiveness of the Domain Reconstruction Classification Network (DRCN) approach on disaster tweets. DRCN adapts information from target data by reconstructing it with an autoencoder. Experimental results using a sequence-to-sequence autoencodershow that the DRCN approach can improve the performance of both supervised and domain adaptation baseline models.

Traditional post-disaster assessment of damage heavily relies on expensive GIS data, especially remote sensing image data. In recent years, social media has become a rich source of disaster information that may be useful in assessing damage at a lower cost. Such information includes text (e.g., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams. The use of social media images to assess disaster damage is limited. In this paper, we propose a novel approach, based on convolutional neural networks and class activation maps, to locate damage in a disaster image and to quantify the degree of the damage. Our proposed approach enables the use of social network images for post-disaster damage assessment, and provides an inexpensive and feasible alternative to the more expensive GIS approach.