We present a system for anomaly detection in histopathological images. In
histology, normal samples are usually abundant, whereas anomalous
(pathological) cases are scarce or not available. Under such settings,
one-class classifiers trained on healthy data can detect out-of-distribution
anomalous samples. Such approaches combined with pre-trained Convolutional
Neural Network (CNN) representations of images were previously employed for
anomaly detection (AD). However, pre-trained off-the-shelf CNN representations
may not be sensitive to abnormal conditions in tissues, while natural
variations of healthy tissue may result in distant representations. To adapt
representations to relevant details in healthy tissue we propose training a CNN
on an auxiliary task that discriminates healthy tissue of different species,
organs, and staining reagents. Almost no additional labeling workload is
required, since healthy samples come automatically with aforementioned labels.
During training we enforce compact image representations with a center-loss
term, which further improves representations for AD. The proposed system
outperforms established AD methods on a published dataset of liver anomalies.
Moreover, it provided comparable results to conventional methods specifically
tailored for quantification of liver anomalies. We show that our approach can
be used for toxicity assessment of candidate drugs at early development stages
and thereby may reduce expensive late-stage drug attrition.
Igor Zingman, Birgit Stierstorfer, Charlotte Lempp, Fabian Heinemann