Multifunctional aggregation network of cell nuclei segmentation aiming histopathological diagnosis assistance: A new MA-Net construction.

Automated diagnostic systems can enhance the accuracy and efficiency of pathological diagnoses, nuclear segmentation plays a crucial role in computer-aided diagnosis systems for histopathology. However, achieving accurate nuclear segmentation is challenging due to the complex background tissue structures and significant variations in cell morphology and size in pathological images. In this study, we have proposed a U-Net based deep learning model, called MA-Net(Multifunctional Aggregation Network), to accurately segmenting nuclei from H&E stained images. In contrast to previous studies that focused on improving a single module of the network, we applied feature fusion modules, attention gate units, and atrous spatial pyramid pooling to the encoder and decoder, skip connections, and bottleneck of U-Net, respectively, to enhance the network's performance in nuclear segmentation. The dice coefficient loss was used during model training to enhance the network's ability to segment small objects. We applied the proposed MA-Net to multiple public datasets, and comprehensive results showed that this method outperforms the original U-Net method and other state-of-the-art methods in nuclei segmentation tasks. The source code of our work can be found in https://github.com/LinaZhaoAIGroup/MA-Net.

Vision transformers motivating superior OAM mode recognition in optical communications.

Orbital angular momentum (OAM) has recently obtained tremendous research interest in free-space optical communications (FSO). During signal transmission within the free-space link, atmospheric turbulence (AT) poses a significant challenge as it diminishes the signal strength and introduce intermodal crosstalk, significantly reducing OAM mode detection accuracy. This issue directly impacts the performance of OAM-based communication systems and leads to a reduction in received information. To address this critical bottleneck of low mode recognition accuracy in OAM-based FSO-communications, a deep learning method based on vision transformers (ViT) is proposed for what we believe is for the first time. Designed carefully by numerous experts, the advanced self-attention mechanism of ViT captures more global information from the input image. To train the model, pretraining on a large dataset, named IMAGENET is conducted. Subsequently, we performed fine-tuning on our specific dataset, consisting of OAM beams that have undergone varying AT strengths. The computer simulation shows that based on ViT method, the multiple OAM modes can be recognized with a high accuracy (nearly 100%) under weak-to-moderate turbulence and with almost 98% accuracy even under long transmission distance with strong turbulence (C N2=1×10-14). Our findings highlight that leveraging ViT enables robust detection of complex OAM beams, mitigating the adverse effects caused by atmospheric turbulence.