Abstract Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accurate survival model from electronic health records is challenging. With this motivation, we address this issue and provide a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we introduce a simple effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African–Americans and white patients. The results indicate that our approach outperforms significantly than baseline models.
Based on our review, there exist no study to develop a survival analysis approach using both deep learning and active learning. We address this gap in the literature to propose a deep active learning framework for survival analysis. However, There are some studies that develop deep active learning methods for other machine learning tasks. For example, @cite_1 developed a semi-supervised learning framework termed active deep network (ADN) for sentiment analysis. They used restricted Boltzmann machines (RBM) for feature learning based on labeled reviews and large amount of unlabeled reviews, then applied gradient-descent based supervised learning for fine tuning and constructing semi-supervised framework. Finally they used active learning in their framework to improve model performance. In the other study, @cite_5 proposed a deep active learning approach using Deep Belief Network (DBN) for classifying hyperspectral images in remote sensing application. A summary of our review has been illustrated in Table which indicates no research have been developed to address a survival approach using deep learning and active learning.
@cite_5 @cite_1
101422005 2891179298 2060099436 2762941833
This paper presents a novel semi-supervised learning algorithm called Active Deep Networks (ADN), to address the semi-supervised sentiment classification problem with active learning. First, we propose the semi-supervised learning method of ADN. ADN is constructed by Restricted Boltzmann Machines (RBM) with unsupervised learning using labeled data and abundant of unlabeled data. Then the constructed structure is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Second, we apply active learning in the semi-supervised learning framework to identify reviews that should be labeled as training data. Then ADN architecture is trained by the selected labeled data and all unlabeled data. Experiments on five sentiment classification datasets show that ADN outperforms the semi-supervised learning algorithm and deep learning techniques applied for sentiment classification. We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images. In comparison, state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors. All networks were trained using labeled CT images from 377 patients with non-small cell lung cancer obtained from the Cancer Imaging Archive and unlabeled T2w MRIs from a completely unrelated cohort of 6 patients with pre-treatment and 36 on-treatment scans. Next, we combined 6 labeled pre-treatment MRI scans with the synthesized MRIs to boost tumor segmentation accuracy through semi-supervised learning. Semi-supervised training of cycle-GAN produced a segmentation accuracy of 0.66 computed using Dice Score Coefficient (DSC). Our method trained with only synthesized MRIs produced an accuracy of 0.74 while the same method trained in semi-supervised setting produced the best accuracy of 0.80 on test. Our results show that tumor-aware adversarial domain adaptation helps to achieve reasonably accurate cancer segmentation from limited MRI data by leveraging large CT datasets. Abstract In natural language processing community, sentiment classification based on insufficient labeled data is a well-known challenging problem. In this paper, a novel semi-supervised learning algorithm called active deep network (ADN) is proposed to address this problem. First, we propose the semi-supervised learning framework of ADN. ADN is constructed by restricted Boltzmann machines (RBM) with unsupervised learning based on labeled reviews and abundant of unlabeled reviews. Then the constructed structure is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Second, in the semi-supervised learning framework, we apply active learning to identify reviews that should be labeled as training data, then using the selected labeled reviews and all unlabeled reviews to train ADN architecture. Moreover, we combine the information density with ADN, and propose information ADN (IADN) method, which can apply the information density of all unlabeled reviews in choosing the manual labeled reviews. Experiments on five sentiment classification datasets show that ADN and IADN outperform classical semi-supervised learning algorithms, and deep learning techniques applied for sentiment classification. With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks. However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model @math and a discriminative model @math . We treat @math as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. @math can produce numerous images that are similar to the training data; therefore, @math can learn better representations of remotely sensed images using the training data provided by @math . The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.