Liver tumor detection and classification from abdominal ultrasound images with CenterNet using contrastive learning

Abdominal ultrasound examination is considered to be highly challenging because of its need to diagnose from moving images taken while handling devices. Previous method consisted of a two-stage inference step where tumors in the input ultrasound image was detected and then the cropped area was classified. However, this previous method may be inaccurate because the tumour detection model is not suitable due to the inability to use global features for classification against the cropped diagnostic image. Therefore, we propose a method that uses SimSiam to pretrain CenterNet and infer using only a single model. The proposed method improves classification accuracy by 3%, and improves memory usage and inference speed by 50% and 33% respectively.