bibtext: @InProceedings{ Kan_BGFYOLO_MICCAI2024,
                   author = { Kang, Ming and Ting, Chee-Ming and Ting, Fung Fung and Phan, Raphaël C.-W. },
                   title = { { BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection. In this paper, we develop a novel BGF-YOLO architecture by incorporating Bi-level Routing Attention (BRA), Generalized feature pyramid networks (GFPN), and Fourth detecting head into YOLOv8. BGF-YOLO contains an attention mechanism to focus more on important features, and feature pyramid networks to enrich feature representation by merging high-level semantic features with spatial details. Furthermore, we investigate the effect of different attention mechanisms and feature fusions, detection head architectures on brain tumor detection accuracy. Experimental results show that BGF-YOLO gives a 4.7% absolute increase of mAP50 compared to YOLOv8x, and achieves state-of-the-art on the brain tumor detection dataset Br35H. The code is available at https://github.com/mkang315/BGF-YOLO.
title: BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
authors: Kang, Ming Ting, Chee-Ming Ting, Fung Fung Phan, Raphaël C.-W.