Polyp-Mamba代码

### Polyp-Mamba 代码实现概述 Polyp-Mamba 是一种基于状态空间模型 (SSM) 的方法，专门用于医学图像处理中的息肉分割任务。该模型通过结合二元交叉熵损失 \(L_{BCE}\) 和加权 IoU 损失 \(L_{Iou}\)，并通过自适应权重调节实现了高性能和快速收敛[^3]。 以下是 Polyp-Mamba 的核心组件及其对应的代码片段： #### 总体结构 Polyp-Mamba 结合了一个编码器-解码器架构，其中编码器负责特征提取，而解码器则通过跳跃连接恢复细节并生成最终的分割图[^4]。 ```python import torch.nn as nn import torch class Encoder(nn.Module): def __init__(self, channels=[64, 128, 256]): super(Encoder, self).__init__() self.patch_merging_layers = nn.ModuleList([ PatchMerging(channels[i]) for i in range(len(channels)-1) ]) def forward(self, x): features = [] for layer in self.patch_merging_layers: x = layer(x) features.append(x) return features class Decoder(nn.Module): def __init__(self, channels=[256, 128, 64], skip_channels=[256, 128, 64]): super(Decoder, self).__init__() self.patch_expanding_layers = nn.ModuleList([ PatchExpanding(skip_channels[i], channels[i]) for i in range(len(channels)) ]) def forward(self, encoder_features): x = None for idx, layer in enumerate(reversed(self.patch_expanding_layers)): if x is None: x = encoder_features[-idx-1] else: x = layer(torch.cat([x, encoder_features[-idx-1]], dim=1)) return x class FinalProjectionLayer(nn.Module): def __init__(self, input_channel, output_channel): super(FinalProjectionLayer, self).__init__() self.up_sample = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True) self.projection = nn.Conv2d(input_channel, output_channel, kernel_size=1) def forward(self, x): x = self.up_sample(x) x = self.projection(x) return x class PolypMambaModel(nn.Module): def __init__(self, enc_chs=[64, 128, 256], dec_chs=[256, 128, 64], n_classes=1): super(PolypMambaModel, self).__init__() self.encoder = Encoder(enc_chs) self.decoder = Decoder(dec_chs[::-1], enc_chs[:-1]) self.final_projection = FinalProjectionLayer(dec_chs[0], n_classes) def forward(self, x): encoded_features = self.encoder(x) decoded_output = self.decoder(encoded_features) final_output = self.final_projection(decoded_output) return final_output ``` #### 损失函数定义 为了优化 Polyp-Mamba 的性能，采用了组合损失函数的形式，即二元交叉熵损失和加权 IoU 损失的线性组合。 ```python def binary_cross_entropy_loss(pred, target): bce_loss_fn = nn.BCEWithLogitsLoss() return bce_loss_fn(pred, target) def weighted_iou_loss(pred, target): intersection = (pred * target).sum(dim=(1, 2, 3)) union = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) - intersection epsilon = 1e-7 loss = ((intersection + epsilon) / (union + epsilon)).mean() return 1 - loss def total_loss(pred, target, lambda1=1.0, lambda2=1.0): bce_loss = binary_cross_entropy_loss(pred, target) iou_loss = weighted_iou_loss(pred, target) return lambda1 * bce_loss + lambda2 * iou_loss ``` ### §相关问题§ 1. 如何调整超参数 \(\lambda_1\) 和 \(\lambda_2\) 来进一步提升 Polyp-Mamba 的性能？ 2. 在实际部署中，如何加速 Polyp-Mamba 的推理过程而不显著降低精度？ 3. 是否可以通过迁移学习将 Polyp-Mamba 扩展到其他医疗影像分割任务？ 4. 如果训练数据不足，能否利用半监督或无监督技术增强 Polyp-Mamba 的泛化能力？ 5. 对于大规模数据集，如何优化 Polyp-Mamba 的训练效率？