MLMSA: Multi-Level and Multi-Scale Attention for Lesion Detection in Endoscopy

The advancement of deep learning techniques has significantly improved abnormality detection in gastrointestinal (GI) endoscopy. However, this imaging process comes with challenges due to the complex nature of GI abnormalities. The wide variety of abnormalities in terms of type, color, texture, shape, and scale of lesions makes it difficult to accurately detect them in different scenarios. Furthermore, the presence of multiple types of lesions within the same region create complex scenarios that complicate abnormality detection. Additionally, differentiating early-stage cancers from non-cancerous lesions is a significant challenge even for experienced professionals. The simultaneous identification of cancers, particularly early-stage ones, and non-cancerous lesions within the same region remains a challenging issue in GI endoscopy imaging. In this study, we discover that multiple types of lesions exhibit a scale-sensitive characteristic that can be leveraged by multi-level feature-based deep learning models. Hence, we propose the use of a multi-level and multi-scale attention (MLMSA) neck module in a deep learning network. The MLMSA module utilizes multiple levels of features extracted from the backbone network to generate processed multi-level features that assist the detection head. By integrating the MLMSA module into the deep learning framework, our goal is to enhance the detection and differentiation of lesions, particularly the early-stage cancer, thereby advancing the capabilities of GI endoscopy imaging. Our experiment results show that integrating the MLMSA module leads to a significant improvement in the detection of GI abnormalities, providing compelling evidence for the enhanced performance achieved through the utilization of the MLMSA module in our approach.