A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms

• URL: http://arxiv.org/abs/2004.11726v1
• Date: Fri, 24 Apr 2020 13:06:47 GMT
• Title: A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms
• Authors: Sarath Chandra K, Arunava Chakravarty, Nirmalya Ghosh, Tandra Sarkar, Ramanathan Sethuraman, Debdoot Sheet
• Abstract summary: Mammograms are commonly employed in the large scale screening of breast cancer. We propose a two-stage Multiple Instance Learning framework for image-level detection of malignancy. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the imagelevel classification task.
• Score: 13.842620686759616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level detection of malignancy is a challenging task given the small size of the mass regions and difficulty in discriminating between malignant, benign mass and healthy dense fibro-glandular tissue. To address these issues, we explore a two-stage Multiple Instance Learning (MIL) framework. A Convolutional Neural Network (CNN) is trained in the first stage to extract local candidate patches in the mammograms that may contain either a benign or malignant mass. The second stage employs a MIL strategy for an image level benign vs. malignant classification. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the imagelevel classification task using a five-fold cross-validation on the INbreast dataset. Restricting the MIL only to the candidate patches extracted in Stage 1 led to a significant improvement in classification performance in comparison to a dense extraction of patches from the entire mammogram.

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