Abstract
Cancerous region detection and subtyping in whole-slide images (WSIs) are fundamental for renal cell carcinoma (RCC) diagnosis. The main challenge in the development of automated RCC diagnostic systems is the lack of large-scale datasets with precise annotations. In this paper, we propose a framework that employs a semi-supervised learning (SSL) method to accurately detect cancerous regions with a novel annotation method called Minimal Point-Based (Min-Point) annotation. The predicted results are efficiently utilized by a hybrid loss training strategy in a classification model for subtyping. The annotator only needs to mark a few cancerous and non-cancerous points in each WSI. The experiments on three significant subtypes of RCC proved that the performance of the cancerous region detector trained with the Min-Point annotated dataset is comparable to the classifiers trained on the dataset with full cancerous region delineation. In subtyping, the proposed model outperforms the model trained with only whole-slide diagnostic labels by 12% in terms of the testing f1-score. We believe that our “detect then classify” schema combined with the Min-Point annotation would set a standard for developing intelligent systems with similar challenges.
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Acknowledgements
This work has been supported by National Key Research and Development Program of China (2018YFC0910404); National Natural Science Foundation of China (61772409); The consulting research project of the Chinese Academy of Engineering (The Online and Offline Mixed Educational Service System for “The Belt and Road” Training in MOOC China); Project of China Knowledge Centre for Engineering Science and Technology; The innovation team from the Ministry of Education (IRT_17R86); and the Innovative Research Group of the National Natural Science Foundation of China (61721002). The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
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Gao, Z., Puttapirat, P., Shi, J., Li, C. (2020). Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_42
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