Abstract
Previous works mainly address the medical datasets with image-wise labels or pixel-wise labels. However, it is difficult to train a model with only image-wise labels, and pixel-wise labels commonly refer to the high expense of annotations. A feasible solution is to make a compromise between data annotation and the performance. In this paper, we propose a cascaded convolutional neural network framework to classify partially annotated pathological images. A segmentation model is trained with the partially annotated samples to detect cancer regions, which are re-identified by a patch-wise classification network. Finally, the segmentation and classification results are combined to make the final image-wise classification. Several experiments are conducted on a landmark medical image dataset with partial annotations. We obtain a classification accuracy of 99.51%, which significantly outperforms other existing methods.
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Notes
- 1.
The challenge is held by Shanghai Big Data Alliance and Center for Applied Information Communication Technology (CAICT), the home page is http://www.datadreams.org/racerace3.html.
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Acknowledgments
This work was supported by National Key Research and Development Program of China 2017YFB1002203, NSFC No. 61572451, and No. 61390514, Fok Ying Tung Education Foundation WF2100060004 and Youth Innovation Promotion Association CAS CX2100060016.
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Cui, Y., Wang, Z., Yu, G., Tian, X. (2018). Partially Annotated Gastric Pathological Image Classification. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_44
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