Current screening of mammography results in a high recall rate. Furthermore, distinguishing between BI-RADS 3 and BI-RADS 4 is a challenge for radiologists. In order to help radiologists’ diagnosis, researches of CAD system recently have shown that methods of deep learning can significantly improve lesion detection, segmentation, and classification. However, there is not enough evidence to show that deep learning models can reduce the high recall rate because few researches provide the performance of cases in BI-RADS 3 and BI-RADS 4. Moreover, few researches extended the current models to involve images in CC and MLO in a single prediction. Thus, we proposed convolutional neural networks to classify breast cancer. Our model could predict images in four input sizes. Besides, we extended our model to consider images in CC and MLO in a single prediction. To validate our models, we split the data depending on patients rather than images. Our training set was composed of 4255 images, and test set contained 355 images that were proven by biopsy and callback. The overall performance of human experts yielded on an accuracy of 65.3% while our model achieved a better accuracy of 79.6%. Besides, the performance of cases in BI-RADS 3 and 4 by human experts was accuracy of 54.1%, but our model maintained a high accuracy of 75.7%. When we combined images in CC and MLO in the single prediction, we achieved AUC of 0.86.
In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on fourphases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.
The detection and the evaluation of the shape of liver from abdominal computed tomography (CT) images are fundamental tasks in the computer-assisted liver surgery planning such as radiation therapy. However, the segmentation of the liver still remains many challenges to be solved, such as ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we developed an automatic liver segmentation model based on 3D U-net network. Some preprocessing steps were done to elevate the performance of our protocol first. Also, an approximate liver map was generated by calculating the gradient of CT images. The area which had high possibility to be liver was select as the training set to make sure the balance of data. Then, a deep learning U-net structure was applied for the processed training data. Finally, some post-processing methods, which include k-means clustering and morphology algorithms, was applied in our protocol. Our protocol showed the results with high structure similarity index (SSIM), dice score coefficient and peak signal-to noise ratio (PSNR) of liver segmentation model, demonstrating the potential clinical applicability of the proposed approach.