This work proposes a novel multi-scale attention-enhanced dual-task network, MSA-DTNet, to simultaneously address two critical tasks, segmentation and registration. MSA-DTNet is designed for high-resolution 3D MRI data to incorporate multi-scale convolutions to capture both local and global features and attention mechanisms to enhance the model’s focus on key anatomical structures. By jointly optimizing segmentation and registration tasks, our network improves anatomical consistency and overall performance in medical image processing. The segmentation decoder produces high-quality segmentation maps, while the registration decoder outputs a displacement field for aligning images with a reference. A novel hybrid loss function is also proposed to optimize the model during training. The experiments on the brain MRI dataset demonstrate that MSA-DTNet outperforms existing state-of-the-art networks in terms of dice score (DSC), intersection over union (IoU), precision and recall in segmentation, and DSC and mean squared error (MSE) in registration tasks. Our model also achieves significant performance improvements, even with limited labeled data, by leveraging semi-supervised learning.

The brain-computer interface (BCI) establishes a connection between a device and the human brain, with electroencephalography (EEG) signal is being used as the most common means for such a communication. We use EEG signal data that has a very limited number of samples for the motor imagery (MI) classification task. This paper proposes a novel densely connected residual graph convolutional network (DenseResGCN) and uses it in developing a few-shot learning method called FewShotEEG method. Our proposed method is capable of classifying the limited EEG signal data into four MI classes. The proposed method outperforms the state-of-the-arts few-shot methods in terms of the accuracy.

In this paper, we propose a novel convolutional neural network called MAGNet that employs multi-scale and global attention mechanisms for the task of medical image segmentation. This network is shown effectively to handle the segmentation task of an image of a given modality provided the network is suitably trained using a training set of the same modality. Experiments are performed to train the proposed network using three different training sets of images (CT, colonoscopy, and non-mydriatic 3CCD images), each acquired from a different imaging technique, resulting in three different trained models. The three trained models are tested on the respective test sets. Each model is shown to significantly outperform the state-of-the-art networks in terms of intersection over union, dice coefficient, and accuracy.