“…Deep learning has been widely developed in the medical field for classification or segmentation tasks [25][26][27]. Classification can be used to identify automatically the kind of cancer from which the patient is suffering [28,29] or the relevant outcomes after treatment, such as survival expectation [30] or relation to the treatment [31]. Recurrence in cancer after treatment is one of the main concerns for physicians [32], as it can dramatically impact the outcome for patients and their life expectancy.…”
In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis–Havrda–Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications. Shannon cross-entropy is widely used as a loss function for most neural networks applied to the segmentation, classification and detection of images. Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy. In this work, we compare these two entropies through a medical application for predicting recurrence in patients with head–neck and lung cancers after treatment. Based on both CT images and patient information, a multitask deep neural network is proposed to perform a recurrence prediction task using cross-entropy as a loss function and an image reconstruction task. Tsallis–Havrda–Charvat cross-entropy is a parameterized cross-entropy with the parameter α. Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy for α=1. The influence of this parameter on the final prediction results is studied. In this paper, the experiments are conducted on two datasets including in total 580 patients, of whom 434 suffered from head–neck cancers and 146 from lung cancers. The results show that Tsallis–Havrda–Charvat entropy can achieve better performance in terms of prediction accuracy with some values of α.
“…Deep learning has been widely developed in the medical field for classification or segmentation tasks [25][26][27]. Classification can be used to identify automatically the kind of cancer from which the patient is suffering [28,29] or the relevant outcomes after treatment, such as survival expectation [30] or relation to the treatment [31]. Recurrence in cancer after treatment is one of the main concerns for physicians [32], as it can dramatically impact the outcome for patients and their life expectancy.…”
In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis–Havrda–Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications. Shannon cross-entropy is widely used as a loss function for most neural networks applied to the segmentation, classification and detection of images. Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy. In this work, we compare these two entropies through a medical application for predicting recurrence in patients with head–neck and lung cancers after treatment. Based on both CT images and patient information, a multitask deep neural network is proposed to perform a recurrence prediction task using cross-entropy as a loss function and an image reconstruction task. Tsallis–Havrda–Charvat cross-entropy is a parameterized cross-entropy with the parameter α. Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy for α=1. The influence of this parameter on the final prediction results is studied. In this paper, the experiments are conducted on two datasets including in total 580 patients, of whom 434 suffered from head–neck cancers and 146 from lung cancers. The results show that Tsallis–Havrda–Charvat entropy can achieve better performance in terms of prediction accuracy with some values of α.
“…Table 4 summarizes the methods and performance of these advanced models. Doppalapudi et al used only single-period images for lung cancer survival classification in their study [ 8 ]. The artificial neural network, recurrent neural network, and convolutional neural network are used to extract deep features for prediction, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Compared to other types of survival (e.g., overall survival), the improvement in DSS is more responsive to the clinical benefit of a specific disease. The greatest significance of accurate DSS survival prediction lies in that it can lead to guideline coordinate treatments which optimizes survival and effectively avoid excessive treatment and waste of medical resources [ 8 ]. Fig.…”
Section: Introductionmentioning
confidence: 99%
“…Many computational models for predicting cancer survival have been proposed, but most of them use only imaging data to generate prediction models, and clinical information of patients (e.g., age, smoking history, family history, etc.) is not considered [ 8 , 18 , 19 ]. To address the above problems, we propose a DSS prediction model for lung cancer based on multi-period CT images and clinical information.…”
Purpose
Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly.
Methods
In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results.
Results
The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor.
Conclusion
In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.
“…At the same time, the accurate prediction of a disease outcome is one of the most challenging tasks for physicians. Some outstanding research has been successfully applied in breast cancer 6 , hepatocellular carcinoma 7 , lung cancer 8 , and other cancer types automatic recognition and survival prediction tasks 6 – 8 . Similar to cancer research, many machine learning methods have been proposed for the effective diagnosis, prognosis, management, and treatment of diabetes 9 – 11 in the past few years.…”
Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box. As the number of diabetic patients is far less than that of healthy people, the rules obtained by the existing rule extraction methods tend to identify healthy people rather than diabetic patients. To address the problem, a method for extracting reduced rules based on biased random forest and fuzzy support vector machine is proposed. Biased random forest uses the k-nearest neighbor (k-NN) algorithm to identify critical samples and generates more trees that tend to diagnose diabetes based on critical samples to improve the tendency of the generated rules for diabetic patients. In addition, the conditions and rules are reduced based on the error rate and coverage rate to enhance interpretability. Experiments on the Diabetes Medical Examination Data collected by Beijing Hospital (DMED-BH) dataset demonstrate that the proposed approach has outstanding results (MCC = 0.8802) when the rules are similar in number. Moreover, experiments on the Pima Indian Diabetes (PID) and China Health and Nutrition Survey (CHNS) datasets prove the generalization of the proposed method.
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