Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. When cancer is found, the best treatment method is selected considering the cancer subtypes. In this study, we investigated a method to distinguish breast cancers with poor prognosis from those with relatively good prognosis to assist diagnosis and treatment planning. In our previous study, all regions of interest including cancer lesions were resized to the same matrix size, which had caused loss of size and local characteristic information of the lesions. In this study, local patches with the original pixel size were automatically selected during the training in each epoch. The patch sampling could also reduce the effect of class imbalance. The proposed model was tested using 264 cases by a 4-fold cross validation. The result indicates the potential usefulness of the proposed method. The computerized subtype classification may support a prompt treatment planning and proper patient care.
Success of breast cancer treatment is subject to various factors, including cancer stage and cancer grade. The best treatment is selected based on the characteristic of cancer. It is desirable to predict the cancer characteristics and prognostic factors accurately and promptly by diagnostic imaging. The purpose of the study is to investigate the use of multimodality diagnostic images in predicting breast cancer subtypes to assist diagnosis and treatment planning. In this study, we classify lesions into molecular subtypes and simultaneously predict histological grades and invasiveness of the cancers by mammography and breast ultrasound images. Models with different architectures including single input and multi-input layers with single head and multiple head models are compared. The results indicate that use of multimodality images is more predictive than using single modalities. The automatic subtype classification using multimodality images may support a prompt treatment planning and proper patient care.
Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. Once cancer is found, the best treatment is selected based on the characteristic of cancer. In this study, we investigated a method to classify breast cancer lesions into four molecular subtypes to assist diagnosis and treatment planning. Because of a limited number of samples and imbalanced types, the lesions were classified based on the similarities of samples using a contrastive learning. The convolutional neural network (CNN) was trained by self-supervised method using paired views of the same lesions with contrastive loss. The subtype was determined by k-nearest neighbor classifier using deep features obtained by the trained network. The proposed model was tested using 385 cases by a 4-fold cross validation. The results are compared with CNN models without and with pretraining. The result indicates the potential usefulness of the proposed method. The computerized subtype classification may support a prompt treatment planning and proper patient care.
Retrieval of similar cases can help radiologists in efficient diagnosis, treatment planning, and preparation of reports for new cases. In this study, similarities of pairs of lesions were estimated using convolutional neural networks with subjective similarity data. The network was trained with pairs of mammograms (MG), pairs of ultrasound images (US), and both as input data and the corresponding similarity ratings by expert radiologists as teacher data. Based on the estimated similarity, the cases with the highest similarities were retrieved for a test case. The precisions of selecting pathology-matched relevant cases were compared for the networks using different input data. In this study, rather a simple network architecture, which takes a pair or pairs of input images and has one regression output layer corresponding to the similarity, provided higher precisions. The precisions using mammograms, ultrasound images, and both modalities were 0.72, 0.68, and 0.80, respectively. The highest precision was obtained by the use of one network with multimodality image inputs than combining the outputs by two separate networks for MG and US data. Relatively high precision indicates that the presentation of reference images can be useful for assisting breast cancer diagnosis.
Presentation of images similar to a new unknown lesion as a reference can be helpful in medical image diagnosis and treatment planning. We have been investigating a method to determine similarity of breast masses as an image retrieval index for an intelligent image analytic system that may support radiologists’ efficient image interpretation. In order to retrieve perceptually similar images, we have obtained subjective similarity ratings from expert radiologists, which were then used in similarity space modeling and training deep neural networks. In this study, we investigated the use of convolutional neural network to model the similarity space for retrieval of diagnostically relevant reference images and also to directly estimate similarity ratings for pairs of images. The preliminary results show that retrieval performance was slightly better in similarity space modeling method than direct estimation method. These results indicate the potential usefulness of the proposed methods for retrieval of reference images as diagnostic assistance.
Presentation of reference images that are similar to a query image can be helpful in medical image diagnosis and treatment planning. The purpose of this study is to investigate a method for retrieving relevant images of breast masses on mammograms as a diagnostic reference. In our previous studies, subjective similarities for pairs of masses were obtained from experienced radiologists and used as the gold standard for retrieving visually similar images. By use of multidimensional scaling, a subjective similarity space was spanned so that masses that were placed close to a query image can be retrieved as reference images. This method, however, required manual outlines of masses for image feature determination. In this study, we modelled this similarity space using convolutional neural network. The result was evaluated using the leave-one-out cross validation method in terms of the correlation between the subjective ratings and determined similarity measures. The relevance of retrieved images was also evaluated in terms of the precision, which is the fraction of pathology matched images in the retrieved images. The correlation coefficient between the subjective ratings and the determined similarity measure was moderate with 0.735, which was slightly lower than those of the previous methods. The average precision was high with 0.852 when the most similar image was retrieved, which was higher than those in the previous studies. The results indicate the potential usefulness of the proposed method in similar image retrieval of breast masses on mammograms.
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