Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
- URL: http://arxiv.org/abs/2206.03935v2
- Date: Thu, 9 Jun 2022 02:15:17 GMT
- Title: Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
- Authors: Yu Cai, Hao Chen, Xin Yang, Yu Zhou, Kwang-Ting Cheng
- Abstract summary: We propose a novel strategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing both known normal images and unlabeled images.
Experiments on three CXR datasets demonstrate that the proposed DDAD achieves consistent, significant gains and outperforms state-of-the-art methods.
- Score: 29.57501199670898
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Chest X-ray (CXR) is the most typical radiological exam for diagnosis of
various diseases. Due to the expensive and time-consuming annotations,
detecting anomalies in CXRs in an unsupervised fashion is very promising.
However, almost all of the existing methods consider anomaly detection as a
One-Class Classification (OCC) problem. They model the distribution of only
known normal images during training and identify the samples not conforming to
normal profile as anomalies in the testing phase. A large number of unlabeled
images containing anomalies are thus ignored in the training phase, although
they are easy to obtain in clinical practice. In this paper, we propose a novel
strategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing
both known normal images and unlabeled images. The proposed method consists of
two modules, denoted as A and B. During training, module A takes both known
normal and unlabeled images as inputs, capturing anomalous features from
unlabeled images in some way, while module B models the distribution of only
known normal images. Subsequently, the inter-discrepancy between modules A and
B, and intra-discrepancy inside module B are designed as anomaly scores to
indicate anomalies. Experiments on three CXR datasets demonstrate that the
proposed DDAD achieves consistent, significant gains and outperforms
state-of-the-art methods. Code is available at
https://github.com/caiyu6666/DDAD.
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