论文标题
Autoseg-转向自动病理分割的归纳偏见
AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation
论文作者
论文摘要
在医学成像中，经常使用异常或分布外检测方法接近，无效的病理检测方法，其诱导性偏见不是故意针对检测病理学的，因此是该任务的优势。为了解决这个问题，我们提出了Autoseg，该发动机可以产生类似于现实世界病理的特性的各种人工异常。我们的方法可以准确地分割看不见的人造异常，并优于现有的方法，用于在挑战性的胸部X射线图像的现实世界中检测病理学检测。我们通过实验评估了有关医疗分布分析分析挑战2021的方法。
In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task. To tackle this problem, we propose AutoSeg, an engine that can generate diverse artificial anomalies that resemble the properties of real-world pathologies. Our method can accurately segment unseen artificial anomalies and outperforms existing methods for pathology detection on a challenging real-world dataset of Chest X-ray images. We experimentally evaluate our method on the Medical Out-of-Distribution Analysis Challenge 2021.
