Traditional object detection methods always assume both of the training and test data follow the same distribution, but this cannot always be guaranteed in the real world. Domain adaptive methods are proposed to handle this situation. However, existing methods generally ignore the semantic alignment at feature level when they try to align data distributions between source and target domains. In this paper, we propose a novel unsupervised cross-domain object detection method, named Cycle-consistent domain Adaptive Faster RCNN (CA-FRCNN). A couple of Generative Adversarial Nets (GAN) are used to make the features from two domains consistent at both data distribution level and semantic level. Specifically, features from source domain are transformed to the target domain. Then they are aligned with features from target domain. At the same time, target features are handled with similar operations. Furthermore, a cycle-consistent loss is optimized to guarantee that the semantic information is preserved before and after the style translations. In the end, identity module is used to make the feature in source domain equivalent to the reconstructed feature output by the source generator, whose input is from the source domain. For the feature in the target domain, similar identity is required. Experiments on multiple datasets show that our method performs better than previous state-of-the-art methods.

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.

Alzheimer’s disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD. The aim of this article is to present the current state of the art in the diagnosis of AD using 3D CNN models and neuroimaging modalities, focusing on the 3D CNN architectures and classification methods used, and to highlight potential future research topics. To give the reader a better overview of the content mentioned in this review, we briefly introduce the commonly used imaging datasets and the fundamentals of CNN architectures. Then we carefully analyzed the existing studies on AD diagnosis, which are divided into two levels according to their inputs: 3D subject-level CNNs and 3D patch-level CNNs, highlighting their contributions and significance in the field. In addition, this review discusses the key findings and challenges from the studies and highlights the lessons learned as a roadmap for future research. Finally, we summarize the paper by presenting some major findings, identifying open research challenges, and pointing out future research directions.

Lung cancer is the main malignant tumour affecting the health of residents in China. Automatically discriminating benign and malignant pulmonary nodules can facilitate the early detection of lung cancer, which reduces lung cancer mortality. The rising quantity of public available lung CT datasets made it possible to use deep learning approaches for lung nodules malignancy classification. Unlike most of the previous models that focused on 2D convolutional neural nets (CNN), here we explore the use of the DenseNet architecture with 3D filters and pooling kernels. The performance of the proposed nodule classification was evaluated on publicly available LUNA16 dataset, a subset of lung image database consortium and image database resource initiative dataset (LIDC/IDRI). It achieved a 92.4% classification accuracy. The proposed method provides an independent module with encouraging prediction accuracy that can be easily incorporated with a lung cancer computer-aided diagnosis system.