We previously introduced the Radon Cumulative Distribution Transform (RCDT) as a novel image transformation to highlight the subtle difference between left and right mammograms to detect mammographically-occult (MO) cancer from women with dense breasts and negative screening mammograms. This study developed deep convolutional neural networks (CNN) as classifiers for estimating the probability of having MO cancer. We acquired screening mammograms of 333 women (97 unilateral MO cancer) with dense breasts and at least two consecutive mammograms and used the immediate prior mammograms, which radiologists interpreted as negative. We divided our dataset into a training, a validation, and a test set with ratios of 0.72:0.08:0.2. We applied RCDT on the left and right mammograms of each view. We applied inverse radon transform to represent the resulting RCDT images in the image domain. We then fine-tuned a VGG16 network pretrained on ImageNet using the resulting images per each view. Using the same images, we also developed a traditional classifier using handcrafted features per each view. The CNNs achieved areas under the receiver operating characteristic (AUC) curve of 0.74 and 0.69 for CC view and MLO view, respectively. The traditional classifiers from handcrafted features achieved AUCs of 0.5 and 0.64 for CC view and MLO view, respectively. We averaged the scores from the top three classifiers and achieved an AUC of 0.81 on the test set. In conclusion, we showed that inverse radon transformed RCDT images hold information to detect MO cancer and deep CNNs could learn such information.
Juhun Lee and Robert M. Nishikawa, "Detecting mammographically-occult cancer in women with dense breasts using deep convolutional neural network and Radon cumulative distribution transform," Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095003 (Presented at SPIE Medical Imaging: February 17, 2019; Published: 13 March 2019); https://doi.org/10.1117/12.2512446.
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