Based on the survey and discussion outcomes, we formulated a design space encompassing visualization thumbnails, and then carried out a user study using four types of visualization thumbnails derived from this space. The study's conclusions show that different components of charts influence how effectively readers are drawn to and comprehend the visual representations of thumbnails. We also uncover a variety of thumbnail design approaches focusing on effectively combining chart components, including a data summary with highlights and data labels, as well as a visual legend with text labels and Human Recognizable Objects (HROs). Our conclusions culminate in design principles that facilitate the creation of compelling thumbnail images for news stories brimming with data. Our contribution can thus be considered a preliminary stage in the provision of structured guidelines for crafting compelling thumbnail designs for data stories.
Recent translational research efforts within the field of brain-machine interfaces (BMI) are indicative of the possibility for improving the lives of people with neurological ailments. The prevailing trend in BMI technology is a dramatic increase in the number of recording channels—thousands now—leading to a massive generation of raw data. Subsequently, the need for high-bandwidth data transmission arises, contributing to higher power consumption and thermal management challenges for implanted systems. Thus, on-implant compression and/or feature extraction are becoming crucial to manage this increasing bandwidth, but they also necessitate further power restrictions – the power consumed for data reduction must not exceed the power saved by bandwidth reduction. Intracortical BMIs often leverage spike detection as a common technique for feature extraction. Employing a firing-rate-based approach, this paper introduces a novel spike detection algorithm. This algorithm is uniquely suited for real-time applications due to its inherent hardware efficiency and the absence of external training. Various datasets are utilized to benchmark existing methods against key performance and implementation metrics, encompassing detection accuracy, adaptability in extended operational environments, power consumption, area utilization, and channel scalability. The algorithm is first tested on a reconfigurable hardware (FPGA) platform, then transferred to a digital ASIC implementation employing both 65 nm and 018μm CMOS technologies. In a 65nm CMOS technology, a 128-channel ASIC design takes up 0.096 mm2 of silicon space and draws 486µW of power, fueled by a 12V power supply. The adaptive algorithm's spike detection accuracy on a common synthetic dataset reaches 96%, proving its effectiveness without any training process.
In terms of prevalence, osteosarcoma is the most common malignant bone tumor, marked by high malignancy and frequent misdiagnosis. Pathological imagery plays a pivotal role in the diagnostic process. Medicolegal autopsy Still, currently, underdeveloped regions experience a shortage of expert pathologists, impacting the reliability and speed of diagnostic processes. The prevailing research on pathological image segmentation often overlooks the disparities in staining techniques, the scarcity of data, and an absence of medical context. In an effort to improve the diagnosis of osteosarcoma in areas lacking resources, an intelligent system for aiding in the diagnosis and treatment of osteosarcoma using pathological images, ENMViT, is proposed. To normalize mismatched images with limited GPU resources, ENMViT utilizes KIN. Traditional data augmentation techniques, such as image cleaning, cropping, mosaic generation, Laplacian sharpening, and others, address the challenge of insufficient data. Image segmentation is executed using a multi-path semantic segmentation network composed of Transformer and CNN components. The spatial edge offset within the image space is factored into the loss function's design. Ultimately, the connecting domain's dimensions dictate the noise filtering process. Central South University provided over 2000 osteosarcoma pathological images for experimentation in this paper. Experimental findings underscore this scheme's robust performance throughout each stage of osteosarcoma pathological image processing. The segmentation results, boasting a 94% higher IoU than comparative models, underscores its significant impact within the medical industry.
For proper diagnosis and treatment of intracranial aneurysms (IAs), the segmentation of IAs is paramount. In spite of this, the technique employed by clinicians to manually identify and pinpoint IAs is extremely labor-intensive and inefficient. The research presented here details the development of a deep-learning framework, FSTIF-UNet, for the segmentation of IAs in un-reconstructed 3D rotational angiography (3D-RA) scans. check details Three hundred patients with IAs from Beijing Tiantan Hospital were selected to have their 3D-RA sequences examined in this study. Taking cues from radiologists' clinical skills, a Skip-Review attention mechanism is proposed to repeatedly merge the long-term spatiotemporal characteristics of multiple images with the most apparent IA features (selected by a preliminary detection network). To fuse the short-term spatiotemporal characteristics of the selected 15 three-dimensional radiographic (3D-RA) images from their equally-spaced viewing angles, a Conv-LSTM is used. The two modules' functionality is essential for fully fusing the 3D-RA sequence's spatiotemporal information. Regarding network segmentation, the FSTIF-UNet model achieved a DSC of 0.9109, IoU of 0.8586, Sensitivity of 0.9314, Hausdorff distance of 13.58, and an F1-score of 0.8883. The time taken per case was 0.89 seconds. Baseline networks are outperformed by FSTIF-UNet in IA segmentation, showing a substantial enhancement in performance. The Dice Similarity Coefficient (DSC) improved from 0.8486 to 0.8794. In clinical diagnosis, the proposed FSTIF-UNet system provides radiologists with a practical method.
The sleep-related breathing disorder known as sleep apnea (SA) is associated with a variety of complications, including instances of pediatric intracranial hypertension, psoriasis, and, unfortunately, sudden death. Therefore, the proactive identification and treatment of SA can effectively mitigate the risk of malignant complications. Portable monitoring, a widely used technique, facilitates the evaluation of sleep quality by individuals outside of a hospital environment. PM facilitates the collection of single-lead ECG signals, which are the basis of this study on SA detection. BAFNet, a bottleneck attention-based fusion network, is designed with five core components: the RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, a global query generation mechanism, a feature fusion module, and a classification component. Employing fully convolutional networks (FCN) with cross-learning, we aim to extract the feature representation from RRI/RPA segments. To regulate the flow of information between RRI and RPA networks, a global query generation method employing bottleneck attention is presented. The SA detection process's efficacy is boosted by the implementation of a hard sample selection method, employing k-means clustering. The experimental outcomes indicate that BAFNet produces results on par with, and potentially better than, current leading SA detection techniques. For sleep condition monitoring via home sleep apnea tests (HSAT), BAFNet is likely to prove quite beneficial, with a strong potential. The project's source code, for the Bottleneck-Attention-Based-Fusion-Network-for-Sleep-Apnea-Detection, is publicly accessible at https//github.com/Bettycxh/Bottleneck-Attention-Based-Fusion-Network-for-Sleep-Apnea-Detection.
A novel contrastive learning methodology for medical image analysis is presented, which employs a unique approach to selecting positive and negative sets from labels available in clinical data. Diverse data labels are employed in the medical profession, playing varying roles in the diagnostic and therapeutic processes. Two specific labeling types are represented by clinical labels and biomarker labels. Clinical labels are more easily obtained in large quantities because they are consistently collected during routine medical care; the collection of biomarker labels, conversely, depends heavily on specialized analysis and expert interpretation. Prior work in ophthalmology has revealed a link between clinical parameters and biomarker structures identifiable from optical coherence tomography (OCT) scans. genetic linkage map This relationship is exploited by utilizing clinical data as pseudo-labels for our dataset without biomarker designations, allowing for the selection of positive and negative samples for training a base network with a supervised contrastive loss function. The backbone network, utilizing this strategy, learns a representational space commensurate with the distribution of clinical data present. The network is subsequently fine-tuned using a limited biomarker-labeled dataset, with cross-entropy loss minimized, to classify key disease markers directly from OCT images produced by this method. Our method for this concept involves a linear combination of clinical contrastive losses, which we detail here. We compare our methods to leading self-supervised techniques in a novel setting, utilizing biomarkers exhibiting varying degrees of granularity. The total biomarker detection AUROC shows a significant improvement, reaching a high of 5%.
Medical image processing is essential for the integration of healthcare within the metaverse and the real world. The field of medical image processing is demonstrating keen interest in self-supervised denoising, which employs sparse coding methods, and does not necessitate large-scale training datasets. Self-supervised methods presently in use often fall short in performance and operational speed. The weighted iterative shrinkage thresholding algorithm (WISTA), a self-supervised sparse coding method, is presented in this paper to enable state-of-the-art denoising performance. Using only a single noisy image, the model's learning process does not leverage noisy-clean ground-truth image pairs. Instead, to further enhance the denoising process, we build a deep neural network (DNN) implementation of the WISTA algorithm, yielding the WISTA-Net architecture.