Computer-aided diagnostics has benefited from the development of deep
learning-based computer vision techniques in these years. Traditional
supervised deep learning methods assume that the test sample is drawn from the
identical distribution as the training data. However, it is possible to
encounter out-of-distribution samples in real-world clinical scenarios, which
may cause silent failure in deep learning-based medical image analysis tasks.
Recently, research has explored various out-of-distribution (OOD) detection
situations and techniques to enable a trustworthy medical AI system. In this
survey, we systematically review the recent advances in OOD detection in
medical image analysis. We first explore several factors that may cause a
distributional shift when using a deep-learning-based model in clinic
scenarios, with three different types of distributional shift well defined on
top of these factors. Then a framework is suggested to categorize and feature
existing solutions, while the previous studies are reviewed based on the
methodology taxonomy. Our discussion also includes evaluation protocols and
metrics, as well as the challenge and a research direction lack of exploration.

深度学习在计算机辅助诊断中得到了应用，但在实际临床场景中可能遇到分布偏移导致的静默失败问题，而最近的研究则探索了各种解决方案，包括分类和评估协议，以及缺乏探索的研究方向。

医学图像分析中的外域检测：一项调查

Out-of-distribution Detection in Medical Image Analysis: A survey

Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed
from radiographic assessment after clinical examination. However, a visual
evaluation made by a practitioner suffers from subjectivity and is highly
dependent on the experience. Computer-aided diagnostics (CAD) could improve the
objectivity of knee radiographic examination. The first essential step of knee
OA CAD is to automatically localize the joint area. However, according to the
literature this task itself remains challenging. The aim of this study was to
develop novel and computationally efficient method to tackle the issue. Here,
three different datasets of knee radiographs were used (n = 473/93/77) to
validate the overall performance of the method. Our pipeline consists of two
parts: anatomically-based joint area proposal and their evaluation using
Histogram of Oriented Gradients and the pre-trained Support Vector Machine
classifier scores. The obtained results for the used datasets show the mean
intersection over the union equal to: 0.84, 0.79 and 0.78. Using a high-end
computer, the method allows to automatically annotate conventional knee
radiographs within 14-16ms and high resolution ones within 170ms. Our results
demonstrate that the developed method is suitable for large-scale analyses.

本研究旨在开发一种新的计算方法，通过基于解剖学的联合区提案和方向梯度直方图的评估及其预先训练的支持向量机分类器的得分来自动定位关节区域，提高膝关节放射成像的客观性，结果表明该方法适用于大规模分析。