LoRIS - Weakly-Supervised Anomaly Detection for Ultrasound Images

This paper presents LoRIS (Localized Reconstruction-by-Inpainting with Single-mask), a novel weakly-supervised anomaly detection technique designed to identify knee joint recess distension in musculoskeletal ultrasound images, which are noisy and unbalanced (as distended cases are rarer). In this context, supervised techniques require a high number of annotated images of both classes (distended and nondistended). On the other hand, we show that existing unsupervised anomaly detection techniques, which can be trained with images from a single class, are ineffective and often unable to correctly localize the anomaly. To overcome these issues, LoRIS is trained with nondistended images only and uses the recess bounding box as location prior to guide the reconstruction. Experimental results show that LoRIS outperforms state-of-the-art unsupervised anomaly detection techniques. When compared to a state-of-the-art fully supervised solution, LoRIS presents similar performance but has two key advantages: during training it requires images from a single class only, and it also outputs the recess segmentation, without the need for segmentation annotations.