Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-networks that better fit the medical lesion detection problem to be discovered? In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. 3) Relation transfer module incorporates the semantic relationship and transfers the relevant contextual information with an interpretable the graph thus alleviates the problem of lack of annotations for all types of lesions. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.

本文介绍了一种新的ElixirNet，包括三个组件：1）TruncatedRPN，用于降低假阳性；2）Auto-lesion Block，自定义于医学图像中，包含区域提议之间的关系感知操作；3）Relation transfer模块，转移相关的上下文信息来缓解所有类型病变的缺乏注释问题，实验证明该方法在DeepLesion和Kits19上的灵敏度和精度均比FPN有所提升。

ElixirNet：医学损伤检测的关系感知网络架构自适应