Convolutional neural networks (CNNs) have been used quite successfully for semantic segmentation of brain tumors. However, current CNNs and attention mechanisms are stochastic in nature and neglect the morphological indicators used by radiologists to manually annotate regions of interest. In this paper, we introduce a channel and spatial wise asymmetric attention (CASPIAN) by leveraging the inherent structure of tumors to detect regions of saliency. To demonstrate the efficacy of our proposed layer, we integrate this into a well-established convolutional neural network (CNN) architecture to achieve higher Dice scores, with less GPU resources. Also, we investigate the inclusion of auxiliary multiscale and multiplanar attention branches to increase the spatial context crucial in semantic segmentation tasks. The resulting architecture is the new CASPIANET++, which achieves Dice Scores of 91.19%, 87.6% and 81.03% for whole tumor, tumor core and enhancing tumor respectively. Furthermore, driven by the scarcity of brain tumor data, we investigate the Noisy Student method for segmentation tasks. Our new Noisy Student Curriculum Learning paradigm, which infuses noise incrementally to increase the complexity of the training images exposed to the network, further boosts the enhancing tumor region to 81.53%. Additional validation performed on the BraTS2020 data shows that the Noisy Student Curriculum Learning method works well without any additional training or finetuning.

卷积神经网络 (CNN) 已非常成功地用于脑肿瘤的语义分割。然而，当前的 CNN 和注意力机制本质上是随机的，并且忽略了放射科医生用来手动注释感兴趣区域的形态学指标。在本文中，我们通过利用肿瘤的固有结构来检测显着区域，引入了通道和空间不对称注意（CASPIAN）。为了证明我们提出的层的功效，我们将其集成到一个完善的卷积神经网络 (CNN) 架构中，以使用更少的 GPU 资源获得更高的 Dice 分数。此外，我们研究了包含辅助多尺度和多平面注意分支以增加语义分割任务中至关重要的空间上下文。由此产生的架构是新的 CSPIANET++，它对整个肿瘤、肿瘤核心和增强肿瘤的 Dice 分数分别达到 91.19%、87.6% 和 81.03%。此外，由于脑肿瘤数据稀缺，我们研究了用于分割任务的嘈杂学生方法。我们新的 Noisy Student Curriculum Learning 范式逐渐注入噪声以增加暴露于网络的训练图像的复杂性，进一步将增强肿瘤区域提高到 81.53%。对 BraTS2020 数据执行的额外验证表明，Noisy Student Curriculum Learning 方法在没有任何额外培训或微调的情况下运行良好。我们研究了用于分割任务的嘈杂学生方法。我们新的 Noisy Student Curriculum Learning 范式逐渐注入噪声以增加暴露于网络的训练图像的复杂性，进一步将增强肿瘤区域提高到 81.53%。对 BraTS2020 数据执行的额外验证表明，Noisy Student Curriculum Learning 方法在没有任何额外培训或微调的情况下运行良好。我们研究了用于分割任务的嘈杂学生方法。我们新的 Noisy Student Curriculum Learning 范式逐渐注入噪声以增加暴露于网络的训练图像的复杂性，进一步将增强肿瘤区域提高到 81.53%。对 BraTS2020 数据执行的额外验证表明，Noisy Student Curriculum Learning 方法在没有任何额外培训或微调的情况下运行良好。