Developing a task-oriented deep convolutional neural network application towards estimating near-term breast cancer risk: preliminary work

Md Belayat Hossain; Juhun Lee

doi:10.1117/12.2654196

7 April 2023 Developing a task-oriented deep convolutional neural network application towards estimating near-term breast cancer risk: preliminary work

Md Belayat Hossain, Juhun Lee

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650J (2023) https://doi.org/10.1117/12.2654196
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

The purpose of this study was to develop a loss function that can drive a given CNN to achieve high sensitivity (or recall) for identifying women with a high-risk of having breast cancer. The cross-entropy (CE) loss function is widely used to optimize a CNN for natural scene classification due to its stability. However, CE loss treats each class equally, thus, it may not be suitable to train the CNN to have high sensitivity performance. Therefore, we hypothesized that a loss function based on the Fβ-measure, the weighted harmonic mean of precision and recall, can improve the sensitivity of the resulting CNN model by giving more weight to the recall metric. To do so, we combined CE loss with the Fβ-measure to implement a task-oriented loss function for achieving high sensitivity performance. In this preliminary work, we used a screening mammogram dataset of 2000 scans (1000 recalled lesions;1000 normal). We extracted recalled lesion patches using radiologists’ annotations and normal patches from the center of the breast. We fine-tuned the DenseNet121 network using the image patch dataset with a data split ratio of 0.8:0.1:0.1 for training, validation, and testing. We conducted ROC analysis to evaluate the performance of our proposed model. In the test set, the model with the task-oriented loss function achieved an AUC of 0.90 compared to CE loss (AUC=0.88) alone. The ROC curve of the proposed loss function achieved (a sensitivity of 53% at 98% specificity level) higher sensitivity than the CE loss alone (a sensitivity of 41% at 98% specificity level) for a high specificity area.

Conference Presentation

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Md Belayat Hossain and Juhun Lee "Developing a task-oriented deep convolutional neural network application towards estimating near-term breast cancer risk: preliminary work", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650J (7 April 2023); https://doi.org/10.1117/12.2654196

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