Learning image representations for anomaly detection: application to discovery of histological alterations in drug development. (arXiv:2210.07675v2 [cs.CV] UPDATED)

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 …

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