A Bayesian Approach to BI-RADS Classification of Mammogram Mass with Deep Learning

• Main Authors: Joseph Chang, 張漢庭
• Other Authors: 陳中明
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/jdy75v

碩士 === 國立臺灣大學 === 醫學工程學研究所 === 107 === According to Global Cancer Statistics, breast cancer has been the most commonly diagnosed cancer and also the leading cause of cancer death among females. Recent improvements in medical technology and mammography show that early detections of microcalcifications, structural abnormalities and mass can be identified through mammograms. Studies indicate early detection can effectively reduce breast cancer mortality rate by 20 to 30%. However, it is difficult for radiologists to make consistent and objective evaluations. Consequently, in order to standardize image reporting and reduce confusion in breast imaging interpretations among radiologist, the American College of Radiology established a Breast Image Reporting and Data-analyzing System (BI-RADS), classifying lesions into 7 categories. However, current assessment of breast lesions according to BI-RADS remain qualitative and subjective, with substantial inter and intra-reader variability. In order to improve the accuracy and consistency of mammogram results, many studies have been conducted to build computer-aided diagnosis (CAD) systems using machine learning methods. However, developments and performance of latest classification systems are mostly limited to targeting specific cases and only differentiating lesions into benign or malignant, thus a proper suitable CAD system that outputs results according to BI-RADS with the same manner proceeded as radiologists is required to provide a second opinion in clinical settings. In this study, novel approaches to classify mass lesions into BI-RADS 3 4 5 in mammograms are explored. Among all the BI-RADS categories, consistent results for BI-RADS 3 4 5 is particularly important. Proper reporting of BI-RADS 3 4 5 can not only help early detection and treatment, but also avoid unnecessary biopsies and surgeries. Since deep learning requires large number of training samples, a Bayesian framework has been investigated to see if incorporating malignancy information can help BI-RADS classification performance. Popular techniques such as data augmentation and transfer learning were also used to help avoid overfitting. State-of-the-art models such as VGG16, ResNet50, DenseNet121, and Inception-V3 were tested to determine each of their performance on both malignancy and BI-RADS classifications. For malignancy classification, Inception-V3 outperformed the rest of the networks with an overall accuracy of 0.854, sensitivity of 0.843 and specificity of 0.863. For BI-RADS classification, Inception-V3 also outperformed other networks with an overall accuracy of 0.622. The trained Inception-V3 network was later used as base model and fine-tuned with prior knowledge through a Bayesian framework to regularize the training process with malignancy estimates. This novel approach increased BI-RADS classification performance by 10% with a final overall accuracy of 0.726, the confusion matrix showed sensitivity of BI-RADS 3: 0.701, BI-RADS 4: 0.761 and BI-RADS 5: 0.717. Class activation maps also helped indicate an improvement in localization during prediction. With limited data, the results show that by using a Bayesian approach to incorporate prior- knowledge can help improve the performance of BI-RADS classification.