Machine learning-based radiotherapy time prediction and treatment scheduling management.

PURPOSE: The utility efficiency of medical devices is important, especially for countries such as China, which have a large population and shortage of medical care resources. Radiotherapy devices are among the most valuable and specialized equipment categories and carry enormous treatment loads. In this study, a novel method is proposed to improve the efficiency of a radiotherapy device (linac). Although scheduling management with accurate prediction of the entire treatment time included in each appointment, arrange a reasonable time duration for appointments and save time between patient shifts effectively. Tasks belonging to the treatment and non-treatment groups can be assigned more flexibly based on the availability of time. MATERIAL AND METHODS: Data from 1665 patients, including patient positioning time (PT) and treatment time (TT), were collected in collaboration with the Radiotherapy Center of the Department of Oncology at the Second Affiliated Hospital of Kunming Medical University from November 2020 to August 2021. The features related to PT and TT were extracted and used to train the machine learning-based model to predict PT and TT in independent patients. The prediction results were subsequently applied to a minute-based scheduling tool. CONCLUSION: Artificial intelligence is a promising approach to solve abstract problems with a specialized knowledge background. The results of this study show encouraging prediction outcomes in relation to effective scheduling management and could improve the efficiency of the linac. This successful trial broadens the meaning of medical data and potential future research directions in radiotherapy.

RegFSC-Net: Medical Image Registration via Fourier Transform With Spatial Reorganization and Channel Refinement Network.

Medical image registration is crucial in medical image analysis applications. Recently, U-Net-style networks have been commonly used for unsupervised image registration, predicting dense displacement fields in full-resolution space. However, this process is resource-intensive and time-consuming for high-resolution volumetric image data. To address this challenge, this paper proposes a novel model named RegFSC-Net, which utilizes Fourier transform with spatial reorganization (SR) and channel refinement (CR) network for registration. We embed efficient feature extraction modules SR and CR modules into the encoder, and adopt a parameter-free model to drive the decoder to improve the U-shaped network. Precisely, RegFSC-Net does not directly predict the full-resolution displacement field in space but learns the low-dimensional representation of the displacement field in the bandlimited Fourier domain, which is beneficial in reducing network parameters, memory usage, and computational costs. Experimental results show that RegFSC-Net outperforms various state-of-the-art methods. Specifically, in comparison to the widely recognized Transformer-based method TransMorph, RegFSC-Net utilizes only around 8.2% of its parameters, resulting in a 1.95% higher Dice score and significantly faster inference speeds of 126.67% and 419.99% on GPU and CPU, respectively. Furthermore, we also designed three variants of RegFSC-Net and demonstrated their potential applications in computer-aided diagnosis.

Skeleton-based Human Action Recognition via Large-kernel Attention Graph Convolutional Network.

The skeleton-based human action recognition has broad application prospects in the field of virtual reality, as skeleton data is more resistant to data noise such as background interference and camera angle changes. Notably, recent works treat the human skeleton as a non-grid representation, e.g., skeleton graph, then learns the spatio-temporal pattern via graph convolution operators. Still, the stacked graph convolution plays a marginal role in modeling long-range dependences that may contain crucial action semantic cues. In this work, we introduce a skeleton large kernel attention operator (SLKA), which can enlarge the receptive field and improve channel adaptability without increasing too much computational burden. Then a spatiotemporal SLKA module (ST-SLKA) is integrated, which can aggregate long-range spatial features and learn long-distance temporal correlations. Further, we have designed a novel skeleton-based action recognition network architecture called the spatiotemporal large-kernel attention graph convolution network (LKA-GCN). In addition, large-movement frames may carry significant action information. This work proposes a joint movement modeling strategy (JMM) to focus on valuable temporal interactions. Ultimately, on the NTU-RGBD 60, NTU-RGBD 120 and Kinetics-Skeleton 400 action datasets, the performance of our LKA-GCN has achieved a state-of-the-art level.

Similarity/dissimilarity calculation methods of DNA sequences: A survey.

DNA sequence similarity/dissimilarity analysis is a fundamental task in computational biology, which is used to analyze the similarity of different DNA sequences for learning their evolutionary relationships. In past decades, a large number of similarity analysis methods for DNA sequence have been proposed due to the ever-growing demands. In order to learn the advances of DNA sequence similarity analysis, we make a survey and try to promote the development of this field. In this paper, we first introduce the related knowledge of DNA similarities analysis, including the data sets, similarities distance and output data. Then, we review recent algorithmic developments for DNA similarity analysis to represent a survey of the art in this field. At last, we summarize the corresponding tendencies and challenges in this research field. This survey concludes that although various DNA similarity analysis methods have been proposed, there still exist several further improvements or potential research directions in this field.