Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
| Autor: | Yiqiu Shen, Jason Phang, Nan Wu, S. Gene Kim, Krzysztof J. Geras, Linda Moy, Jungkyu Park, Kyunghyun Cho |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
medicine.diagnostic_test Computer science business.industry Screening mammography Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 010501 environmental sciences medicine.disease 01 natural sciences Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Breast cancer screening 0302 clinical medicine Breast cancer medicine Saliency map Artificial intelligence business Classifier (UML) 0105 earth and related environmental sciences |
| Zdroj: | Machine Learning in Medical Imaging ISBN: 9783030326913 MLMI@MICCAI |
| Popis: | Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings. |
| Databáze: | OpenAIRE |
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