Abstract
Gliomas are the most common and severe malignant tumors of the brain. The diagnosis and grading of gliomas are typically based on MRI images and pathology images. To improve the diagnosis accuracy and efficiency, we intend to design a framework for computer-aided diagnosis combining the two modalities. Without loss of generality, we first take an individual network for each modality to get the features and fuse them to predict the subtype of gliomas. For MRI images, we directly take a 3D-CNN to extract features, supervised by a cross-entropy loss function. There are too many normal regions in abnormal whole slide pathology images (WSI), which affect the training of pathology features. We call these normal regions as noise regions and propose two ideas to reduce them. Firstly, we introduce a nucleus segmentation model trained on some public datasets. The regions that has a small number of nuclei are excluded in the subsequent training of tumor classification. Secondly, we take a noise-rank module to further suppress the noise regions. After the noise reduction, we train a gliomas classification model based on the rest regions and obtain the features of pathology images. Finally, we fuse the features of the two modalities by a linear weighted module. We evaluate the proposed framework on CPM-RadPath2020 and achieve the first rank on the validation set.
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This work was supported by the National Natural Science Foundation of China under Grant 61472393.
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Yin, B., Cheng, H., Wang, F., Wang, Z. (2021). Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology Images. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_41
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DOI: https://doi.org/10.1007/978-3-030-72087-2_41
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