Deep convolutional neural networks show a good prospect in the fertility detection and classification of specific pathogen-free hatching egg embryos in the production of avian influenza vaccine, and our previous work has mainly investigated three factors of networks to push performance: depth, width, and cardinality. However, an important problem that feeble embryos with weak blood vessels interfering with the classification of resilient fertile ones remains. Inspired by fine-grained classification, we introduce the attention mechanism into our model by proposing a dense pixelwise spatial attention module combined with the existing channel attention through depthwise separable convolutions to further enhance the network class-discriminative ability. In our fused attention module, depthwise convolutions are used for channel-specific features learning, and dilated convolutions with different sampling rates are adopted to capture spatial multiscale context and preserve rich detail, which can maintain high resolution and increase receptive fields simultaneously. The attention mask with strong semantic information generated by aggregating outputs of the spatial pyramid dilated convolution is broadcasted to low-level features via elementwise multiplications, serving as a feature selector to emphasize informative features and suppress less useful ones. A series of experiments conducted on our hatching egg dataset show that our attention network achieves a lower misjudgment rate on weak embryos and a more stable accuracy, which is up to 98.3% and 99.1% on 5-day and 9-day old eggs, respectively.