The purpose of this study is to develop a novel computer-aided diagnosis (CAD) scheme to facilitate breast mass classification, which is based on the latest transferring generative adversarial networks (GAN) technology. Although GAN is one of the most popular techniques for image augmentation, it requires a relatively large original dataset to achieve satisfactory results, which may not be available for most of the medical imaging tasks. To address this challenge, we developed a novel transferring GAN, which was built based on the deep convolutional generative adversarial networks (DCGAN). This novel model was first pre-trained on a dataset of non-mass mammogram patches. Then the generator and the discriminator were fine-tuned on the mass dataset. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. We retrospectively assembled a total of 25,000 non-mass patches and 1024 mass images to assess this model, using classification accuracy and receiver operating characteristic (ROC) curve. The results demonstrated that our proposed approach improved the accuracy and area under the ROC curve (AUC) by 6.0% and 3.5% respectively, when compared with the classifiers trained without conventional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on medical imaging tasks with limited datasets.
This study aims to utilize the primary tumor characteristics from CT images to detect lymph node (LN) metastasis for accurately categorizing locally advanced cervical cancer patients (LACC). In clinical practice, LN metastasis is a critical indicator for patients’ prognostic assessment, which is usually investigated by PET/CT (i.e., positron emission tomography/computed tomography) examination. However, the high cost of the PET/CT imaging modality limits its application and also leads to heavy financial burden on patients. Thus it is clinically imperative to develop an economic solution for the LN metastasis identification. For this purpose, a novel image marker was developed, which is based on the primary cervical tumors segmented from CT images. Accordingly, a total of 99 handcrafted features were computed, and an optimal feature set was determined by Laplacian Score (LS) method. Next, a logistic regression model was applied on the optimal feature set to generate a likelihood score for the identification of LN metastasis. Using a retrospective dataset that contains a total of 82 LACC patients, this new model was trained and optimized by leave one out cross validation (LOOCV) strategy. The marker performance was assessed by receiver operator characteristic curve (ROC). The results indicate that the area under the ROC curve (AUC) of this identification model was 0.774±0.050, which demonstrates its strong discriminative power. This study may be able to provide gynecologic oncologists a CT image based low cost clinical marker to identify LN metastasis occurred on LACC patients.
The purpose of this investigation is to verify the feasibility of using deep learning technology to generate an image marker for accurate stratification of cervical cancer patients. For this purpose, a pre-trained deep residual neural network (i.e. ResNet-50) is used as a fixed feature extractor, which is applied to the previously identified cervical tumors depicted on CT images. The features at average pooling layer of the ResNet-50 are collected as initial feature pool. Then discriminant neighborhood embedding (DNE) algorithm is employed to reduce the feature dimension and create an optimal feature cluster. Next, a k-nearest neighbors (k-NN) regression model uses this cluster as input to generate an evaluation score for predicting patient’s response to the planned treatment. In order to assess this new model, we retrospectively assembled the pre-treatment CT images from a number of 97 locally advanced cervical cancer (LACC) patients. The leave one out cross validation (LOOCV) strategy is adopted to train and optimize this new scheme and the receiver operator characteristic curve (ROC) is applied for performance evaluation. The result shows that this new model achieves an area under the ROC curve (AUC) of 0.749 ± 0.064, indicating that the deep neural networks enables to identify the most effective tumor characteristics for therapy response prediction. This investigation initially demonstrates the potential of developing a deep learning based image marker to assist oncologists on categorizing cervical cancer patients for precision treatment.
Deep convolutional neural networks (CNNs) based transfer learning is an effective tool to reduce the dependence on hand-crafted features for handling medical classification problems, which may mitigate the problem of the insufficient training caused by the limited sample size. In this study, we investigated the discrimination power of the features at different CNN levels for the task of classifying epithelial and stromal regions on digitized pathologic slides which are prepared from breast cancer tissue. We extracted the low level and high level features from four different deep CNN architectures namely, AlexNet, Places365-AlexNet, VGG, and GoogLeNet. These features are used as input to train and optimize different classifiers including support vector machine (SVM), random forest (RF), and k-nearest neighborhood (KNN). A number of 15000 regions of interest (ROIs) acquired from the public database are employed to conduct this study. The result was observed that the low-level features of AlexNet, Places365-AlexNet and VGG outperformed the high-level ones, but the situation is in the opposite direction when the GoogLeNet is applied. Moreover, the best accuracy was achieved as 89.7% by the relatively deep layer of max pool 4 of GoogLeNet. In summary, our extensive empirical evaluation may suggest that it is viable to extend the use of transfer learning to the development of high-performance detection and diagnosis systems for medical imaging tasks.
The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.
Predicting metastatic tumor response to chemotherapy at early stage is critically important for improving efficacy of clinical trials of testing new chemotherapy drugs. However, using current response evaluation criteria in solid tumors (RECIST) guidelines only yields a limited accuracy to predict tumor response. In order to address this clinical challenge, we applied Radiomics approach to develop a new quantitative image analysis scheme, aiming to accurately assess the tumor response to new chemotherapy treatment, for the advanced ovarian cancer patients. During the experiment, a retrospective dataset containing 57 patients was assembled, each of which has two sets of CT images: pre-therapy and 4-6 week follow up CT images. A Radiomics based image analysis scheme was then applied on these images, which is composed of three steps. First, the tumors depicted on the CT images were segmented by a hybrid tumor segmentation scheme. Then, a total of 115 features were computed from the segmented tumors, which can be grouped as 1) volume based features; 2) density based features; and 3) wavelet features. Finally, an optimal feature cluster was selected based on the single feature performance and an equal-weighed fusion rule was applied to generate the final predicting score. The results demonstrated that the single feature achieved an area under the receiver operating characteristic curve (AUC) of 0.838±0.053. This investigation demonstrates that the Radiomic approach may have the potential in the development of high accuracy predicting model for early stage prognostic assessment of ovarian cancer patients.
Accurate tumor segmentation is a critical step in the development of the computer-aided detection (CAD) based quantitative image analysis scheme for early stage prognostic evaluation of ovarian cancer patients. The purpose of this investigation is to assess the efficacy of several different methods to segment the metastatic tumors occurred in different organs of ovarian cancer patients. In this study, we developed a segmentation scheme consisting of eight different algorithms, which can be divided into three groups: 1) Region growth based methods; 2) Canny operator based methods; and 3) Partial differential equation (PDE) based methods. A number of 138 tumors acquired from 30 ovarian cancer patients were used to test the performance of these eight segmentation algorithms. The results demonstrate each of the tested tumors can be successfully segmented by at least one of the eight algorithms without the manual boundary correction. Furthermore, modified region growth, classical Canny detector, and fast marching, and threshold level set algorithms are suggested in the future development of the ovarian cancer related CAD schemes. This study may provide meaningful reference for developing novel quantitative image feature analysis scheme to more accurately predict the response of ovarian cancer patients to the chemotherapy at early stage.
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