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Myopia is a serious threat to eye health and can even cause blindness. It is important to grade myopia and carry out targeted intervention. Nowadays, various studies using deep learning models based on optical coherence tomography (OCT) images to screen for high myopia. However, since regions of interest (ROIs) of pre-myopia and low myopia on OCT images are relatively small, it is rather difficult to use OCT images to conduct detailed myopia grading. There are few studies using OCT images for more detailed myopia grading. To address these problems, we propose a novel attention-based multi-scale feature fusion network named AMFF for myopia grading using OCT images. The proposed AMFF mainly consists of five modules: a pre-trained vision transformer (ViT) module, a multi-scale convolutional module, an attention feature fusion module, an Avg-TopK pooling module and a fully connected (FC) classifier. Firstly, unsupervised pre-training of ViT on the training set can better extract feature maps. Secondly, multi-scale convolutional layers further extract multi-scale feature maps to obtain more receptive fields and extract scale-invariant features. Thirdly, feature maps of different scales are fused through channel attention and spatial attention to further obtain more meaningful features. Lastly, the most prominent features are obtained by the weighted average of the highest activation values of each channel, and then they are used to classify myopia through a fully connected layer. Extensive experiments show that our proposed model has the superior performance compared with other state-of-the-art myopia grading models. [ABSTRACT FROM AUTHOR]

Purpose: Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients. Methods: A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan–Meier method and compared with the observed PFS probability. Results: Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785–0.851), 0.752 (95% CI: 0.638–0.865), and 0.717 (95% CI: 0.641–0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822–0.895), 0.769 (95% CI: 0.642–0.896), and 0.730 (95% CI: 0.634–0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups. Conclusion: Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning. [ABSTRACT FROM AUTHOR]

Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (∼127 GB) to that of the DNN parameters (∼20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy (HR) can potentially lead to vision loss if left untreated. Early screening and treatment are critical in reducing the risk of vision loss. The computer-aided diagnostic system presents an opportunity to improve the efficiency and reliability of HR screening and diagnosis, particularly given the shortage of specialized medical professionals and the challenges faced by primary care physicians in making precise diagnoses. A notable barrier to the development of such diagnostic algorithms is the lack of publicly available benchmarks and datasets. To address these issues, we organized a challenge named “HRDC—Hypertensive Retinopathy Diagnosis Challenge” in conjunction with the Computer Graphics International (CGI) 2023 conference. The challenge provided a fundus image dataset for two clinical tasks: hypertension classification and HR classification, with each task containing 1000 images. This paper presents a concise summary and analysis of the submitted methods and results for the two challenge tasks. For hypertension classification, the best performing algorithm achieved a Kappa score of 0.3819, an F1 score of 0.6337, and a specificity of 0.8472. For HR classification, the best performing algorithm achieved a Kappa score of 0.4154, an F1 score of 0.6122, and a specificity of 0.8444. We also explored an ensemble approach to the top-ranking methods, which further improved performance beyond the individual best performing algorithm for each task. The challenge results show that there is room for further optimization of these methods, but the insights and methodologies derived from this challenge provide valuable directions for developing more precise and reliable classification models for hypertension and HR. [ABSTRACT FROM AUTHOR]

Swept-source optical coherence tomography (SS-OCT) is widely used to diagnose high myopia due to its advantage in imaging the ocular anatomic structures. Although the scleral deformation provides information on the risk of the high myopia, further validation of these highly promising findings in clinical studies has been limited by the current semi-automated software, which requires human input, and the automatic analysis of the scleral structure is quite challenging due to the ambiguous boundaries. To address these challenges, we propose a deep contour attention network (DCANet) for automatic segmentation of scleral deformation structure. Specifically, we design a scale-aware attention feature fusion module to achieve cross-scale feature fusion, which can facilitate the network to learn complementary information from multi-scale features. In addition, we develop a pyramid feature enhancement module to allow the network to learn global contextual features through the combination of receptive field and attention mechanism, and we also propose a boundary heatmap label to enrich boundary information. We evaluate the performance of the proposed method on two in-house SS-OCT datasets. In addition to the multiple metrics that are used for evaluating the segmentation performance, including Jaccard similarity coefficient, dice similarity coefficient and boundary distance error, we also propose length similarity coefficient and angle similarity coefficient to evaluate the length estimation and angle estimation, respectively. The experimental results show that our method can effectively improve the segmentation performance, and our DCANet achieves the overall best performance on two datasets compared with other state-of-the-art networks. Our findings motivate the development of clinically applicable deep learning systems for the prediction of high myopia progression on the basis of the scleral phenotypes from SS-OCT images. [ABSTRACT FROM AUTHOR]