Computer Aided Image Diagnosis Research Articles

Automatic creation of annotations for chest radiographs based on the positional information extracted from radiographic image reports

Background and objectiveIn this study, we tried to create a machine-learning method that detects disease lesions from chest X-ray (CXR) images using a data set annotated with extracted CXR reports information. We set the nodule as the target disease lesion. Manually annotating nodules is costly in terms of time. Therefore, we used the report information to automatically produce training data for the object detection task. MethodsFirst, we use semantic segmentation model PSP-Net to recognize lung fields described in the CXR reports. Next, a classification model ResNeSt-50 is used to discriminate the nodule in segmented right and left field. It also can provide attention map by Grad-Cam. If the attention region corresponds to the location of the nodule in the CXR reports, an attention bounding box is generated. Finally, object detection model Faster-RCNN was performed using generated attention bounding box. The bounding boxes predicted by Faster-RCNN were filtered to satisfy the location extracted from CXR reports. ResultsFor lung field segmentation, a mean intersection of union of 0.889 was achieved in our best model. 15,156 chest radiographs are used for classification. The area under the receiver operating characteristics curve was 0.843 and 0.852 for the left and right lung, respectively. The detection precision of the generated attention bounding box was 0.341 to 0.531 depending on the binary setting for attention map. Through object detection process, the detection precisions of the bounding boxes were improved to 0.567 to 0.800. ConclusionWe successfully generated bounding boxes with nodule on CXR images based on the positional information of the diseases extracted from the CXR reports. Our method has the potential to provide bounding boxes for various lung lesions which can reduce the annotation burden for specialists. Short abstractMachine learning for computer aided image diagnosis requires annotation of images, but manual annotation is time-consuming for medical doctor. In this study, we tried to create a machine-learning method that creates bounding boxes with disease lesions on chest X-ray (CXR) images using the positional information extracted from CXR reports. We set the nodule as the target lesion. First, we use PSP-Net to segment the lung field according to the CXR reports. Next, a classification model ResNeSt-50 was used to discriminate the nodule in segmented lung field. We also created an attention map using the Grad-Cam algorithm. If the area of attention matched the area annotated by the CXR report, the coordinate of the bounding box was considered as a possible nodule area. Finally, we used the attention information obtained from the nodule classification model and let the object detection model trained by all of the generated bounding boxes. Through object detection model, the precision of the bounding boxes to detect nodule is improved.

Medical image diagnosis of lung cancer by multi-layered GMDH-type neural network self-selecting functions

In this study, a revised Group Method of Data Handling (GMDH)-type neural network self-selecting functions is applied to the computer aided image diagnosis (CAD) of lung cancer. The GMDH-type neural network algorithm has an ability of self-selecting optimum neural network architecture from three neural network architectures, such as sigmoid function neural network, radial basis function neural network and polynomial neural network. The GMDH-type neural network also has abilities of self-selecting the number of layers, the number of neurons in hidden layers and useful input variables. This algorithm is applied to CAD of lung cancers, and it is shown that this algorithm is useful for the CAD, and is very easy to apply to practical complex problems because optimum neural network architecture is automatically organized.

Hybrid Multi-layered GMDH-type Neural Network Using Principal Component Regression Analysis and its Application to Medical Image Diagnosis of Liver Cancer

In this study, a hybrid multi-layered Group Method of Data Handling (GMDH)-type neural network algorithm using principal component-regression analysis is proposed and applied to the computer aided image diagnosis (CAD) of liver cancer. In the GMDH-type neural network, a heuristic self-organization method that is a type of evolutionary computation, is used to organize the neural network architecture. In this revised GMDH-type neural network, the optimum neural network architecture is automatically organized from three types of neural network architectures, such as the sigmoid function neural network, the radial basis function (RBF) network and the polynomial neural network architecture, by the heuristic self-organization method. Furthermore, the structural parameters such as the number of layers, the number of neurons in hidden layers and useful input variables, are automatically determined using the heuristic self-organization method. In the revised GMDH-type neural network proposed in this paper, the principal component-regression analysis is used to protect multi-colinearity which has occurred in the learning calculations of neurons, and accurate and stable prediction values are obtained. This new algorithm is applied to the medical image diagnosis of liver cancer. In this application, two types of neural network architectures fitting the complexity of the multi-detector row CT (MDCT) medical images, are automatically organized using the revised GMDH-type neural network algorithm The first neural network recognizes and extracts the liver regions from the MDCT images of the liver, and the second neural network recognizes and extracts the liver cancer regions. These results are compared with the conventional sigmoid function neural network trained using the back propagation method, and this GMDH-type neural network algorithm is shown to be useful for CAD of liver cancer.