Medical image analysis is one of the research fields that had huge benefits from deep learning in recent years. To earn a good performance, the learning model requires large scale data with full annotation. However, it is a big burden to collect a sufficient number of labeled data for the training. Since there are more unlabeled data than labeled ones in most of medical applications, self-supervised learning has been utilized to improve the performance. However, most of current methods for self-supervised learning try to understand only semantic features of the data, but have not fully utilized properties inherent in medical images. Specifically, in CT or MR images, the spatial or structural information contained in the dataset has not been fully considered. In this paper, we propose a novel method for self-supervised learning in medical image analysis that can exploit both semantic and spatial features at the same time. The proposed method is experimented in the problems of organ segmentation, intracranial hemorrhage detection and the results show the effectiveness of the method.
Objective-this article presents a new computerized scheme that aims to accurately and robustly separate left and right lungs on CT examinations.Methods-we developed and tested a method to separate the left and right lungs using sequential CT information and a guided dynamic programming algorithm using adaptively and automatically selected start point and end point with especially severe and multiple connections.Results-the scheme successfully identified and separated all 827 connections on the total 4034 CT images in an independent testing dataset of CT examinations. The proposed scheme separated multiple connections regardless of their locations, and the guided dynamic programming algorithm reduced the computation time to approximately 4.6% in comparison with the traditional dynamic programming and avoided the permeation of the separation boundary into normal lung tissue.Conclusions-The proposed method is able to robustly and accurately disconnect all connections between left and right lungs and the guided dynamic programming algorithm is able to remove redundant processing.
Background
The Cox proportional hazards model is commonly used to predict hazard ratio, which is the risk or probability of occurrence of an event of interest. However, the Cox proportional hazard model cannot directly generate an individual survival time. To do this, the survival analysis in the Cox model converts the hazard ratio to survival times through distributions such as the exponential, Weibull, Gompertz or log-normal distributions. In other words, to generate the survival time, the Cox model has to select a specific distribution over time.
Results
This study presents a method to predict the survival time by integrating hazard network and a distribution function network. The Cox proportional hazards network is adapted in DeepSurv for the prediction of the hazard ratio and a distribution function network applied to generate the survival time. To evaluate the performance of the proposed method, a new evaluation metric that calculates the intersection over union between the predicted curve and ground truth was proposed. To further understand significant prognostic factors, we use the 1D gradient-weighted class activation mapping method to highlight the network activations as a heat map visualization over an input data. The performance of the proposed method was experimentally verified and the results compared to other existing methods.
Conclusions
Our results confirmed that the combination of the two networks, Cox proportional hazards network and distribution function network, can effectively generate accurate survival time.
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