In this paper, we propose a method, named BRAU-Net, to solve the pubic
symphysis-fetal head segmentation task. The method adopts a U-Net-like pure
Transformer architecture with bi-level routing attention and skip connections,
which effectively learns local-global semantic information. The proposed
BRAU-Net was evaluated on transperineal Ultrasound images dataset from the
pubic symphysis-fetal head segmentation and angle of progression (FH-PS-AOP)
challenge. The results demonstrate that the proposed BRAU-Net achieves
comparable a final score. The codes will be available at
this https URL

本文提出了一种名为 BRAU-Net 的方法，用于解决耻骨联合 - 胎头分割任务。该方法采用了一种类似 U-Net 的纯 Transformer 架构，并结合了双层路由注意力和跳跃连接，有效地学习了局部 - 全局语义信息。通过对耻骨联合 - 胎头分割和进展角度（FH-PS-AOP）挑战的经会阴超声图像数据集进行评估，结果表明所提出的 BRAU-Net 达到了相当的最终得分。代码将在此 https URL 上提供。

使用全 Transformer 和双层路由注意力进行耻骨联合与胎儿头部分割

Pubic Symphysis-Fetal Head Segmentation Using Full Transformer with  Bi-level Routing Attention

You Only Look Once (YOLO)-based object detectors have shown remarkable
accuracy for automated brain tumor detection. In this paper, we develop a novel
BGFG-YOLO architecture by incorporating Bi-level Routing Attention (BRA),
Generalized feature pyramid networks (GFPN), Forth detecting head, and
Generalized-IoU (GIoU) bounding box regression loss into YOLOv8. BGFG-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
BGFG-YOLO gives a 3.4% 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 this https URL

该论文介绍了一种新颖的 BGFG-YOLO 架构，通过将 Bi-level Routing Attention（BRA）、Generalized feature pyramid networks（GFPN）、Forth detecting head 和 Generalized-IoU（GIoU）bounding box regression loss 结合到 YOLOv8 中，实现了对自动脑肿瘤检测的高准确性。实验结果显示，与 YOLOv8x 相比，BGFG-YOLO 在脑肿瘤检测数据集 Br35H 上的 mAP50 绝对增加了 3.4％，达到了 state-of-the-art 水平。