Abstract
In current oncological workflows of clinical decision making and treatment management, biopsy is the only way to confirm the abnormality of cancer. On the purpose of reducing unnecessary biopsies and diagnostic burden, we propose a patient-wise feature transfer model for learning the relationship of phenotypes between radiological images and pathological images. We hypothesize that high-level features from the same patient are possible to be linked between modalities of different image scales. We integrate multiple feature transfer blocks between CNN-based networks with single-/multi-modality radiological images and pathological images in an end-to-end training framework. We refer to our method as “augmented radiology” because the inference model only requires radiological images as input while the prediction result can be linked to specific pathological phenotypes. We apply the proposed method to glioma grading (high-grade vs. low-grade) and train the feature transfer model by using patient-wise multimodal MRI images and pathological images. Evaluation results show that the proposed method can achieve pathological tumor grading score in high accuracy (AUC 0.959) only given the radiological images as input.
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Li, Z., Ogino, M. (2020). Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. DART DCL 2020 2020. Lecture Notes in Computer Science(), vol 12444. Springer, Cham. https://doi.org/10.1007/978-3-030-60548-3_3
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DOI: https://doi.org/10.1007/978-3-030-60548-3_3
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