A mammography classification model trained from image labels only
DOI:
https://doi.org/10.7557/18.6244Keywords:
deep learning, neural networks, breast cancer screening, weakly supervised localization, high-resolution image classificationAbstract
The Cancer Registry of Norway organises a population-based breast cancer screening program, where 250 000 women participate each year. The interpretation of the screening mammograms is a manual process, but deep neural networks are showing potential in mammographic screening. Most methods focus on methods trained from pixel-level annotations, but these require expertise and are time-consuming to produce. Through the screenings, image level annotations are however readily available. In this work we present a few models trained from image level annotations from the Norwegian dataset: a holistic model, an attention model and an ensemble model. We compared their performance with that of pretrained models based on pixel-level annotations, trained on international datasets. From this we found that models trained on our local data with image-level annotation gave considerably better performance than the pretrained models from external data, although based on pixel-level annotations.
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Copyright (c) 2022 Fredrik Dahl, Marit Holden, Olav Brautaset, Line Eikvil
This work is licensed under a Creative Commons Attribution 4.0 International License.
