Visual Computing for Industry Biomedicine and Art最新文献

Cataract is the leading ocular disease of blindness and visual impairment globally. Deep neural networks (DNNs) have achieved promising cataracts recognition performance based on anterior segment optical coherence tomography (AS-OCT) images; however, they have poor explanations, limiting their clinical applications. In contrast, visual features extracted from original AS-OCT images and their transform forms (e.g., AS-OCT-based histograms) have good explanations but have not been fully exploited. Motivated by these observations, an explainable machine learning framework to recognize cataracts severity levels automatically using AS-OCT images was proposed, consisting of three stages: visual feature extraction, feature importance explanation and selection, and recognition. First, the intensity histogram and intensity-based statistical methods are applied to extract visual features from original AS-OCT images and AS-OCT-based histograms. Subsequently, the SHapley Additive exPlanations and Pearson correlation coefficient methods are applied to analyze the feature importance and select significant visual features. Finally, an ensemble multi-class ridge regression method is applied to recognize the cataracts severity levels based on the selected visual features. Experiments on a clinical AS-OCT-NC dataset demonstrate that the proposed framework not only achieves competitive performance through comparisons with DNNs, but also has a good explanation ability, meeting the requirements of clinical diagnostic practice.

Fluorescence endoscopy technology utilizes a light source of a specific wavelength to excite the fluorescence signals of biological tissues. This capability is extremely valuable for the early detection and precise diagnosis of pathological changes. Identifying a suitable experimental approach and metric for objectively and quantitatively assessing the imaging quality of fluorescence endoscopy is imperative to enhance the image evaluation criteria of fluorescence imaging technology. In this study, we propose a new set of standards for fluorescence endoscopy technology to evaluate the optical performance and image quality of fluorescence imaging objectively and quantitatively. This comprehensive set of standards encompasses fluorescence test models and imaging quality assessment protocols to ensure that the performance of fluorescence endoscopy systems meets the required standards. In addition, it aims to enhance the accuracy and uniformity of the results by standardizing testing procedures. The formulation of pivotal metrics and testing methodologies is anticipated to facilitate direct quantitative comparisons of the performance of fluorescence endoscopy devices. This advancement is expected to foster the harmonization of clinical and preclinical evaluations using fluorescence endoscopy imaging systems, thereby improving diagnostic precision and efficiency.

The vision transformer (ViT) architecture, with its attention mechanism based on multi-head attention layers, has been widely adopted in various computer-aided diagnosis tasks due to its effectiveness in processing medical image information. ViTs are notably recognized for their complex architecture, which requires high-performance GPUs or CPUs for efficient model training and deployment in real-world medical diagnostic devices. This renders them more intricate than convolutional neural networks (CNNs). This difficulty is also challenging in the context of histopathology image analysis, where the images are both limited and complex. In response to these challenges, this study proposes a TokenMixer hybrid-architecture that combines the strengths of CNNs and ViTs. This hybrid architecture aims to enhance feature extraction and classification accuracy with shorter training time and fewer parameters by minimizing the number of input patches employed during training, while incorporating tokenization of input patches using convolutional layers and encoder transformer layers to process patches across all network layers for fast and accurate breast cancer tumor subtype classification. The TokenMixer mechanism is inspired by the ConvMixer and TokenLearner models. First, the ConvMixer model dynamically generates spatial attention maps using convolutional layers, enabling the extraction of patches from input images to minimize the number of input patches used in training. Second, the TokenLearner model extracts relevant regions from the selected input patches, tokenizes them to improve feature extraction, and trains all tokenized patches in an encoder transformer network. We evaluated the TokenMixer model on the BreakHis public dataset, comparing it with ViT-based and other state-of-the-art methods. Our approach achieved impressive results for both binary and multi-classification of breast cancer subtypes across various magnification levels (40×, 100×, 200×, 400×). The model demonstrated accuracies of 97.02% for binary classification and 93.29% for multi-classification, with decision times of 391.71 and 1173.56 s, respectively. These results highlight the potential of our hybrid deep ViT-CNN architecture for advancing tumor classification in histopathological images. The source code is accessible: https://github.com/abimouloud/TokenMixer .

With the exponential rise in global air traffic, ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security. Although X-ray baggage monitoring is now standard, manual screening has several limitations, including the propensity for errors, and raises concerns about passenger privacy. To address these drawbacks, researchers have leveraged recent advances in deep learning to design threat-segmentation frameworks. However, these models require extensive training data and labour-intensive dense pixel-wise annotations and are finetuned separately for each dataset to account for inter-dataset discrepancies. Hence, this study proposes a semi-supervised contour-driven broad learning system (BLS) for X-ray baggage security threat instance segmentation referred to as C-BLX. The research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage scans. The proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage scans. More specifically, the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues, effectively identifying concealed prohibited items without entire baggage scans. The multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories, including threat and benign classes. The contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation results. The proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation, yielding 90.04%, 78.92%, and 59.44% in terms of mIoU on GDXray, SIXray, and Compass-XP, respectively. Furthermore, the limitations of the proposed system in extracting precise region segments in intricate noisy settings and potential strategies for overcoming them through post-processing techniques were explored (source code will be available at https://github.com/Divs1159/CNN_BLS .).

This study presents an energy consumption (EC) forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning (FTL). To predict the EC of manufacturing products, particularly from scale-down to scale-up, a general paradigm was first developed by categorizing the overall process into three main sub-steps. The operating electrical power was further formulated as a combinatorial function, based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC. Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power. Considering the interconnections among these factors, the outputs of the neural networks were blended and fused to jointly predict the electrical power. Most innovatively, large artifacts can be decomposed into time-dependent laser-scanning trajectories, which can be further transformed into fusionable information via neural networks, inspired by large language model. Accordingly, transfer learning can deal with either scale-down or scale-up forecasting, namely, FTL with scalability within artifact structures. The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion. The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%. The melting fusion quality was examined using metallographic diagrams. The proposed FTL framework can forecast the EC of scaled structures, which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging (MRI) radiomics feature selection, modelling, and performance. This study used two sets of publicly available prostate cancer MRI = radiomics data (Dataset 1: n = 260; Dataset 2: n = 100) with Gleason score clinical references. Each dataset was divided into training and holdout testing datasets at a ratio of 7:3 and analysed independently. The clinical references of the training set were permuted at different levels (increments of 5%) and repeated 20 times. Four feature selection algorithms and two classifiers were used to construct the models. Cross-validation was employed for training, while a separate hold-out testing set was used for evaluation. The Jaccard similarity coefficient was used to evaluate feature selection, while the area under the curve (AUC) and accuracy were used to assess model performance. An analysis of variance test with Bonferroni correction was conducted to compare the metrics of each model. The consistency of the feature selection performance decreased substantially with the clinical reference permutation. AUCs of the trained models with permutation particularly after 20% were significantly lower (Dataset 1 (with ≥ 20% permutation): 0.67, and Dataset 2 (≥ 20% permutation): 0.74), compared to the AUC of models without permutation (Dataset 1: 0.94, Dataset 2: 0.97). The performances of the models were also associated with larger uncertainties and an increasing number of permuted clinical references. Clinical reference uncertainty can substantially influence MRI radiomic feature selection and modelling. The high accuracy of clinical references should be helpful in building reliable and robust radiomic models. Careful interpretation of the model performance is necessary, particularly for high-dimensional data.

This survey examines the evolution and impact of real-time brainmedia on artistic exploration, contextualizing developments within a historical framework. To enhance knowledge on the entanglement between the brain, mind, and body in an increasingly mediated world, this work defines a clear scope at the intersection of bio art and interactive art, concentrating on real-time brainmedia artworks developed in the 21st century. It proposes a set of criteria and a taxonomy based on historical notions, interaction dynamics, and media art representations. The goal is to provide a comprehensive overview of real-time brainmedia, setting the stage for future explorations of new paradigms in communication between humans, machines, and the environment.

Recently, human motion prediction has gained significant attention and achieved notable success. However, current methods primarily rely on training and testing with ideal datasets, overlooking the impact of variations in the viewing distance and viewing angle, which are commonly encountered in practical scenarios. In this study, we address the issue of model invariance by ensuring robust performance despite variations in view distances and angles. To achieve this, we employed Riemannian geometry methods to constrain the learning process of neural networks, enabling the prediction of invariances using a simple network. Furthermore, this enhances the application of motion prediction in various scenarios. Our framework uses Riemannian geometry to encode motion into a novel motion space to achieve prediction with an invariant viewing distance and angle using a simple network. Specifically, the specified path transport square-root velocity function is proposed to aid in removing the view-angle equivalence class and encode motion sequences into a flattened space. Motion coding by the geometry method linearizes the optimization problem in a non-flattened space and effectively extracts motion information, allowing the proposed method to achieve competitive performance using a simple network. Experimental results on Human 3.6M and CMU MoCap demonstrate that the proposed framework has competitive performance and invariance to the viewing distance and viewing angle.

Three-dimensional (3D) fetal ultrasound has been widely used in prenatal examinations. Realistic and real-time volumetric ultrasound volume rendering can enhance the effectiveness of diagnoses and assist obstetricians and pregnant mothers in communicating. However, this remains a challenging task because (1) there is a large amount of speckle noise in ultrasound images and (2) ultrasound images usually have low contrasts, making it difficult to distinguish different tissues and organs. However, traditional local-illumination-based methods do not achieve satisfactory results. This real-time requirement makes the task increasingly challenging. This study presents a novel real-time volume-rendering method equipped with a global illumination model for 3D fetal ultrasound visualization. This method can render direct illumination and indirect illumination separately by calculating single scattering and multiple scattering radiances, respectively. The indirect illumination effect was simulated using volumetric photon mapping. Calculating each photon's brightness is proposed using a novel screen-space destiny estimation to avoid complicated storage structures and accelerate computation. This study proposes a high dynamic range approach to address the issue of fetal skin with a dynamic range exceeding that of the display device. Experiments show that our technology, compared to conventional methodologies, can generate realistic rendering results with far more depth information.

Deep learning (DL) has proven to be important for computed tomography (CT) image denoising. However, such models are usually trained under supervision, requiring paired data that may be difficult to obtain in practice. Diffusion models offer unsupervised means of solving a wide range of inverse problems via posterior sampling. In particular, using the estimated unconditional score function of the prior distribution, obtained via unsupervised learning, one can sample from the desired posterior via hijacking and regularization. However, due to the iterative solvers used, the number of function evaluations (NFE) required may be orders of magnitudes larger than for single-step samplers. In this paper, we present a novel image denoising technique for photon-counting CT by extending the unsupervised approach to inverse problem solving to the case of Poisson flow generative models (PFGM)++. By hijacking and regularizing the sampling process we obtain a single-step sampler, that is NFE = 1. Our proposed method incorporates posterior sampling using diffusion models as a special case. We demonstrate that the added robustness afforded by the PFGM++ framework yields significant performance gains. Our results indicate competitive performance compared to popular supervised, including state-of-the-art diffusion-style models with NFE = 1 (consistency models), unsupervised, and non-DL-based image denoising techniques, on clinical low-dose CT data and clinical images from a prototype photon-counting CT system developed by GE HealthCare.