Abstract
This paper proposes a novel network WSI-Net for segmentation and classification of gigapixel breast whole-slide images (WSIs). WSI-Net can segment patches from the WSI into three types, including non-malignant, ductal carcinoma in situ, and invasive ductal carcinoma. It adds a parallel classification branch on the top of the low layer of a semantic segmentation model DeepLab. This branch can fast identify and discard those non-malignant patches in advance and thus the high layer of DeepLab can only focus on the remaining possible cancerous inputs. This strategy can accelerate inference and robustly improve segmentation performance. For training WSI-Net, a hierarchy-aware loss function is proposed to combine pixel-level and patch-level loss, which can capture the pathology hierarchical relationships between pixels in each patch. By aggregating patch segmentation results from WSI-Net, we generate a segmentation map for the WSI and extract its morphological features for WSI-level classification. Experimental results show that our WSI-Net can be fast, robust and effective on our benchmark dataset.
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Notes
- 1.
“Primitive score” means the result on the initial test dataset without resampling.
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Ni, H., Liu, H., Wang, K., Wang, X., Zhou, X., Qian, Y. (2019). WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histopathological Whole-Slide Images. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_5
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