一、本文介绍

本文记录的是利用 $MS{A}^{2}Net$ 中的 MASAG 模块改进 YOLOv11 的多模态融合部分 。

MASAG (Multi - Scale Adaptive Spatial Attention Gate) 模块 通过动态调制空间注意力权重与多尺度感受野 ，实现了 对跨层级特征图中局部细节与全局语义的智能聚合 。将其应用于 YOLOv11 的改进过程中，针对目标检测中 浅层边界特征 与 深层语义信息 的互补性需求， 增强对多尺度目标的特征表达能力 ，提升复杂场景下的检测精度与边界定位准确性。

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二、MASAG 介绍

MSA^2Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation

2.1 设计出发点

1. 传统基于卷积神经网络（CNNs）的方法 难以有效捕捉长距离依赖，且受限于静态感受野 ；
2. 基于Transformer的方法在 捕捉局部表示和上下文方面存在不足 ，计算效率也较低。
3. 虽然已有多种改进方法，但大多只专注于改进编码器或解码器， 未对原始跳跃连接设计进行有效改变 。

因此，需要一种新的模块来优化跳跃连接，更好地融合局部和全局特征，提高医学图像分割的精度。

2.2 结构

• 多尺度特征融合 ：该模块融合局部上下文提取和全局上下文提取，通过深度卷积、扩张卷积拓宽编码器高分辨率空间细节（X）的空间范围，利用通道池化获取解码器语义信息（G）中的广泛上下文信息，将两者整合形成综合特征图，公式为： $$U=Con{v}_{1\times 1}(DW-D(DW(X)))+Con{v}_{1\times 1}([P_{Avg}(G);P_{Max}(G)])$$
• 空间选择 ：将融合后的特征图（U）投影到两个通道，通过通道软max计算空间选择性权重，得到空间选择后的 $X^{\prime }$ 和 $G^{\prime }$ ，并使用两个残差连接优化梯度流和特征利用，公式包括： $$S{W}_i=S(Con{v}_{1\times 1}(U))\forall i\in [1,2]$$ $$X^{\prime }=S{W}_1\otimes X+X,G^{\prime }=S{W}_2\otimes G+G$$
• 空间交互和交叉调制 ：此部分对 $X^{\prime }$ 和 $G^{\prime }$ 进行动态增强，使 $X^{\prime }$ 融入 $G^{\prime }$ 的全局上下文， $G^{\prime }$ 融入 $X^{\prime }$ 的详细上下文，最后将两者相乘融合，公式为： $$X^{\prime \prime }=X^{\prime }\otimes \sigma \left(G^{\prime }\right),G^{\prime \prime }=G^{\prime }\otimes \sigma \left(X^{\prime }\right)$$ $$U^{\prime }=X^{\prime \prime }\otimes G^{\prime \prime }$$
• 重新校准 ：对空间交互和交叉调制得到的融合特征图进行逐点卷积和sigmoid激活，生成注意力图，用其重新校准编码器的初始输入X，公式为： $$X=Con{v}_{1\times 1}\left(\sigma \left(Con{v}_{1\times 1}\left(U^{\prime }\right)\right)\otimes X\right)$$

2.3 优势

• 动态调整感受野 ：能够根据输入动态调整感受野，突出空间相关特征，抑制无关背景细节，提高对不同大小、形状和结构的器官及病变的分割准确性 。
• 有效融合特征 ：通过多尺度融合、空间交互等操作，实现局部和全局特征的有效融合，使模型在分割时能综合考虑详细信息和整体结构，在复杂医学图像中精准分割目标结构。
• 提升模型性能 ：在多个医学数据集（如Synapse数据集和ISIC 2018数据集）上的实验表明，包含MASAG模块的(MSA ^{{2}Net)方法在多项指标上优于或与当前最优方法相当，如在Synapse数据集上，(MSA} {2}Net)的Dice相似系数（DSC）比2D版本的D - LKA高0.48%，比DAE - Former高2.21% ，充分证明了该模块对提升模型性能的有效性。

论文： https://arxiv.org/abs/2407.21640
源码： https://github.com/xmindflow/MSA-2Net

三、MASAG 的实现代码

MASAG 模块 的实现代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F

def autopad(k, p=None, d=1):  # kernel, padding, dilation
    """Pad to 'same' shape outputs."""
    if d > 1:
        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p

class Conv(nn.Module):
    """Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""

    default_act = nn.SiLU()  # default activation

    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
        """Initialize Conv layer with given arguments including activation."""
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()

    def forward(self, x):
        """Apply convolution, batch normalization and activation to input tensor."""
        return self.act(self.bn(self.conv(x)))

    def forward_fuse(self, x):
        """Perform transposed convolution of 2D data."""
        return self.act(self.conv(x))

def num_trainable_params(model):
    nums = sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6
    return nums

class GlobalExtraction(nn.Module):
  def __init__(self,dim = None):
    super().__init__()
    self.avgpool = self.globalavgchannelpool
    self.maxpool = self.globalmaxchannelpool
    self.proj = nn.Sequential(
        nn.Conv2d(2, 1, 1,1),
        nn.BatchNorm2d(1)
    )
  def globalavgchannelpool(self, x):
    x = x.mean(1, keepdim = True)
    return x

  def globalmaxchannelpool(self, x):
    x = x.max(dim = 1, keepdim=True)[0]
    return x

  def forward(self, x):
    x_ = x.clone()
    x = self.avgpool(x)
    x2 = self.maxpool(x_)

    cat = torch.cat((x,x2), dim = 1)

    proj = self.proj(cat)
    return proj

class ContextExtraction(nn.Module):
  def __init__(self, dim, reduction = None):
    super().__init__()
    self.reduction = 1 if reduction == None else 2

    self.dconv = self.DepthWiseConv2dx2(dim)
    self.proj = self.Proj(dim)

  def DepthWiseConv2dx2(self, dim):
    dconv = nn.Sequential(
        nn.Conv2d(in_channels = dim,
              out_channels = dim,
              kernel_size = 3,
              padding = 1,
              groups = dim),
        nn.BatchNorm2d(num_features = dim),
        nn.ReLU(inplace = True),
        nn.Conv2d(in_channels = dim,
              out_channels = dim,
              kernel_size = 3,
              padding = 2,
              dilation = 2),
        nn.BatchNorm2d(num_features = dim),
        nn.ReLU(inplace = True)
    )
    return dconv

  def Proj(self, dim):
    proj = nn.Sequential(
        nn.Conv2d(in_channels = dim,
              out_channels = dim //self.reduction,
              kernel_size = 1
              ),
        nn.BatchNorm2d(num_features = dim//self.reduction)
    )
    return proj
  def forward(self,x):
    x = self.dconv(x)
    x = self.proj(x)
    return x

class MultiscaleFusion(nn.Module):
  def __init__(self, dim):
    super().__init__()
    self.local= ContextExtraction(dim)
    self.global_ = GlobalExtraction()
    self.bn = nn.BatchNorm2d(num_features=dim)

  def forward(self, x, g,):
    x = self.local(x)
    g = self.global_(g)

    fuse = self.bn(x + g)
    return fuse

class MultiScaleGatedAttn(nn.Module):
    # Version 1
  def __init__(self, dims):
    super().__init__()
    dim = min(dims)
    if dims[0] != dims[1]:
        self.conv1 = Conv(dims[0], dim)
        self.conv2 = Conv(dims[1], dim)
    self.multi = MultiscaleFusion(dim)
    self.selection = nn.Conv2d(dim, 2,1)
    self.proj = nn.Conv2d(dim, dim,1)
    self.bn = nn.BatchNorm2d(dim)
    self.bn_2 = nn.BatchNorm2d(dim)
    self.conv_block = nn.Sequential(
        nn.Conv2d(in_channels=dim, out_channels=dim,
                  kernel_size=1, stride=1))

  def forward(self, inputs):
    x, g = inputs
    if x.size(1) != g.size(1):
        x = self.conv1(x)
        g = self.conv2(g)
    x_ = x.clone()
    g_ = g.clone()

    #stacked = torch.stack((x_, g_), dim = 1) # B, 2, C, H, W

    multi = self.multi(x, g) # B, C, H, W

    ### Option 2 ###
    multi = self.selection(multi) # B, num_path, H, W

    attention_weights = F.softmax(multi, dim=1)  # Shape: [B, 2, H, W]
    #attention_weights = torch.sigmoid(multi)
    A, B = attention_weights.split(1, dim=1)  # Each will have shape [B, 1, H, W]

    x_att = A.expand_as(x_) * x_  # Using expand_as to match the channel dimensions
    g_att = B.expand_as(g_) * g_

    x_att = x_att + x_
    g_att = g_att + g_
    ## Bidirectional Interaction

    x_sig = torch.sigmoid(x_att)
    g_att_2 = x_sig * g_att

    g_sig = torch.sigmoid(g_att)
    x_att_2 = g_sig * x_att

    interaction = x_att_2 * g_att_2

    projected = torch.sigmoid(self.bn(self.proj(interaction)))

    weighted = projected * x_

    y = self.conv_block(weighted)

    #y = self.bn_2(weighted + y)
    y = self.bn_2(y)
    return y

---

四、添加步骤

4.1 修改一

① 在 ultralytics/nn/ 目录下新建 AddModules 文件夹用于存放模块代码

② 在 AddModules 文件夹下新建 MultiScaleGatedAttn.py ，将 第三节 中的代码粘贴到此处

4.2 修改二

在 AddModules 文件夹下新建 __init__.py （已有则不用新建），在文件内导入模块： from .MultiScaleGatedAttn import *

4.3 修改三

在 ultralytics/nn/tasks.py 文件中，需要在两处位置添加各模块类名称。

首先：导入模块

然后，在 parse_model函数 中添加如下代码：

        elif m in {MultiScaleGatedAttn}:
            c1 = [ch[x] for x in f]
            c2 = min(c1)
            args = [c1]

---

五、yaml模型文件

5.1 中期融合⭐

📌 此模型的修方法是将MASAG模块应用到YOLOv11的中期融合中。

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
ch: 6
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, IN, []]  # 0
  - [-1, 1, Multiin, [1]]  # 1
  - [-2, 1, Multiin, [2]]  # 2

  - [1, 1, Conv, [64, 3, 2]] # 3-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 4-P2/4
  - [-1, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 6-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 8-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 10-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 12
  - [-1, 2, C2PSA, [1024]] # 13

  - [2, 1, Conv, [64, 3, 2]] # 14-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 15-P2/4
  - [-1, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 17-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 19-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 21-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 23
  - [-1, 2, C2PSA, [1024]] # 24

  - [[7, 18], 1, MultiScaleGatedAttn, []]  # 25 cat backbone P3
  - [[9, 20], 1, MultiScaleGatedAttn, []]  # 26 cat backbone P4
  - [[13, 24], 1, MultiScaleGatedAttn, []]  # 27 cat backbone P5

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 26], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 30

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 25], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 33 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 30], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 36 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 27], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 39 (P5/32-large)

  - [[33, 36, 39], 1, Detect, [nc]] # Detect(P3, P4, P5)

---

六、成功运行结果

打印网络模型可以看到不同的融合层已经加入到模型中，并可以进行训练了。

**YOLOv11-mid-MSGA **：

YOLO11-mid-MSGA summary: 543 layers, 5,149,512 parameters, 5,149,496 gradients, 13.4 GFLOPs

                   from  n    params  module                                       arguments
  0                  -1  1         0  ultralytics.nn.AddModules.multimodal.IN      []
  1                  -1  1         0  ultralytics.nn.AddModules.multimodal.Multiin [1]
  2                  -2  1         0  ultralytics.nn.AddModules.multimodal.Multiin [2]
  3                   1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]
  4                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]
  5                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]
  6                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]
  7                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]
  8                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]
  9                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]
 10                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]
 11                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]
 12                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]
 13                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]
 14                   2  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]
 15                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]
 16                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]
 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]
 18                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]
 20                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]
 21                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]
 22                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]
 23                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]
 24                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]
 25             [7, 18]  1    200199  ultralytics.nn.AddModules.MultiScaleGatedAttn.MultiScaleGatedAttn[[128, 128]]
 26             [9, 20]  1    200199  ultralytics.nn.AddModules.MultiScaleGatedAttn.MultiScaleGatedAttn[[128, 128]]
 27            [13, 24]  1    793607  ultralytics.nn.AddModules.MultiScaleGatedAttn.MultiScaleGatedAttn[[256, 256]]
 28                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
 29            [-1, 26]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 30                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]
 31                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
 32            [-1, 25]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 33                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]
 34                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]
 35            [-1, 30]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 36                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]
 37                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]
 38            [-1, 27]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 39                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]
 40        [33, 36, 39]  1    430867  ultralytics.nn.modules.head.Detect           [1, [64, 128, 256]]
YOLO11-mid-MSGA summary: 543 layers, 5,149,512 parameters, 5,149,496 gradients, 13.4 GFLOPs