PCA-YOLO: A Small Liver Tumor Detection Model with Patch-Contrastive Attention

Abstract

Liver tumors, as one of the most common malignant tumor types, represent a significant clinical challenge, with the detection of small tumors being particularly problematic. Despite the rapid advances in deep learning (DL) offering significant support in reducing the workload of radiologists, current detection models still struggle with the detection of small tumors. This is particularly troubling as these are the cases where even experienced radiologists are more prone to errors, underscoring the critical need for improved accuracy of detection methods in this area. Addressing this critical gap, this article introduces patch-contrastive attention YOLO (PCA-YOLO), an innovative adaptation of the YOLO framework, incorporating a patch-based attention module to specifically target the detection of small liver tumors. Furthermore, we collected a specialized CT dataset focusing exclusively on small liver tumors, complemented with meticulously annotated bounding boxes, to facilitate this study. Our experimental findings demonstrate that our approach achieves a leading mean Average Precision (mAP) score of 77.2\% at a 50\% Intersection Over Union (IoU) threshold, surpassing all current leading detection methods tested against our specialized dataset.