A study of extractive summarization of long documents incorporating local topic and hierarchical information.

In recent years, the transformer-based language models have achieved remarkable success in the field of extractive text summarization. However, there are still some limitations in this kind of research. First, the transformer language model usually regards the text as a linear sequence, ignoring the inherent hierarchical structure information of the text. Second, for long text data, traditional extractive models often focus on global topic information, which poses challenges in how they capturing and integrating local contextual information within topic segments. To address these issues, we propose a long text extractive summarization model that employs a local topic information extraction module and a text hierarchical extraction module to capture the local topic information and document's hierarchical structure information of the original text. Our approach enhances the ability to determine whether a sentence belongs to the summary. In this experiment, ROUGE score is used as the experimental evaluation index, and evaluates the model on three large public datasets. Through experimental validation, the model demonstrates superior performance in terms of ROUGE-1, ROUGE-2, and ROUGE-L scores compared to current mainstream summarization models, affirming the effectiveness of incorporating local topic information and document hierarchical structure into the model.

Registration-free 3D super-resolution generative deep-learning network for fluorescence microscopy imaging.

Volumetric fluorescence microscopy has a great demand for high-resolution (HR) imaging and comes at the cost of sophisticated imaging solutions. Image super-resolution (SR) methods offer an effective way to recover HR images from low-resolution (LR) images. Nevertheless, these methods require pixel-level registered LR and HR images, posing a challenge in accurate image registration. To address these issues, we propose a novel registration-free image SR method. Our method conducts SR training and prediction directly on unregistered LR and HR volume neuronal images. The network is built on the CycleGAN framework and the 3D UNet based on attention mechanism. We evaluated our method on LR (5×/0.16-NA) and HR (20×/1.0-NA) fluorescence volume neuronal images collected by light-sheet microscopy. Compared to other super-resolution methods, our approach achieved the best reconstruction results. Our method shows promise for wide applications in the field of neuronal image super-resolution.

[Research progress and challenges of deep learning in medical image registration].

With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.