Detecting invasive breast carcinoma on dynamic contrast-enhanced MRI

Stefania L. Moroianu; Mirabela Rusu

doi:10.1117/12.2580989

15 February 2021 Detecting invasive breast carcinoma on dynamic contrast-enhanced MRI

Stefania L. Moroianu, Mirabela Rusu

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970F (2021) https://doi.org/10.1117/12.2580989
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Deep learning models have the potential to improve prediction of the presence of invasive breast cancer on MR images. Here we present a transfer learning framework for classifying dynamic contrast-enhanced MR images in two classes: those that have invasive breast carcinoma and those that are noninvasive (including benign findings and indolent cancers). We build and train several models based on a pre-trained VGG16 network and found that fine-tuning the last convolutional block is the best strategy for our small data scenario. Our model was trained and evaluated using 81 female patients who had a pre-operative MRI followed by surgery. All lesions have ground truth labels from the surgical pathology reports. We used a bounding box to generate cropped images centered on the lesion and extract multiple slices per lesion. Our network achieved an AUC of 0.83±0.05, sensitivity 0.83±0.16 and specificity 0.71±0.11 in predicting the presence of invasive cancer in the breast. We compared our results with state-of-the-art methods and found that our model is more accurate in distinguishing invasive from noninvasive lesions. Finally, the visual inspection of the class activation maps allowed us to better understand the decision process of our deep learning classifiers.

Conference Presentation

Citation Download Citation

Stefania L. Moroianu and Mirabela Rusu "Detecting invasive breast carcinoma on dynamic contrast-enhanced MRI", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970F (15 February 2021); https://doi.org/10.1117/12.2580989

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