A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

• URL: http://arxiv.org/abs/2104.10326v1
• Date: Wed, 21 Apr 2021 02:57:02 GMT
• Title: A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
• Authors: Jie Lian and Jingyu Liu and Shu Zhang and Kai Gao and Xiaoqing Liu and Dingwen Zhang and Yizhou Yu
• Abstract summary: Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. We propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. We release ChestX-Det, a chest X-Ray dataset with instance-level annotations (boxes and masks)
• Score: 63.76299770460766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net consists of three relation modules: 1. the anatomical structure relation module encoding spatial relations between diseases and anatomical parts. 2. the contextual relation module aggregating clues based on query-key pair of disease RoI and lung fields. 3. the disease relation module propagating co-occurrence and causal relations into disease proposals. Towards making a practical system, we also provide ChestX-Det, a chest X-Ray dataset with instance-level annotations (boxes and masks). ChestX-Det is a subset of the public dataset NIH ChestX-ray14. It contains ~3500 images of 13 common disease categories labeled by three board-certified radiologists. We evaluate our SAR-Net on it and another dataset DR-Private. Experimental results show that it can enhance the strong baseline of Mask R-CNN with significant improvements. The ChestX-Det is released at https://github.com/Deepwise-AILab/ChestX-Det-Dataset.

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