In light of the survey and discussion, a design space for visualization thumbnails was developed, followed by a user study involving four types of visualization thumbnails, originating from the formulated design space. Different chart elements, according to the study, play a unique role in increasing reader engagement and improving understanding of the thumbnail visualizations presented. Our analysis also reveals a range of thumbnail design strategies for seamlessly integrating chart components, like data summaries with highlights and data labels, along with visual legends with text labels and Human Recognizable Objects (HROs). Our research, ultimately, generates design principles for crafting thumbnail designs that are visually effective for news articles replete with data. Therefore, our contribution constitutes an initial step in providing structured guidance on the design of captivating thumbnails for data-driven narratives.
Recent advancements in brain-machine interface technology (BMI) are showcasing the potential for alleviating neurological disorders through translational efforts. The proliferation of BMI recording channels, now reaching into the thousands, is generating an overwhelming volume of raw data. This correspondingly mandates high data transmission bandwidth, thus increasing power consumption and heat dissipation by implanted systems. To counteract this surge in bandwidth, on-implant compression and/or feature extraction are consequently becoming essential, but this comes with an added power consideration – the energy needed for data reduction must not exceed the energy saved by decreasing bandwidth. Intracortical BMIs frequently employ spike detection, a prevalent feature extraction technique. 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. Diverse datasets are used to benchmark existing methods against key implementation and performance metrics; these metrics encompass detection accuracy, adaptability during sustained deployment, power consumption, area utilization, and channel scalability. After initial validation using a reconfigurable hardware (FPGA) platform, the algorithm is subsequently integrated into a digital ASIC implementation for both 65 nm and 018μm CMOS. The 128-channel ASIC, built using 65nm CMOS technology, occupies a silicon area of 0.096mm2 and draws 486µW of power from a 12V power source. The adaptive algorithm, on a commonly utilized synthetic dataset, showcases a 96% spike detection accuracy, free from the requirement of any prior training.
The common bone tumor, osteosarcoma, displays a high degree of malignancy, unfortunately often leading to misdiagnosis. The interpretation of pathological images is essential for a correct diagnosis. Transbronchial forceps biopsy (TBFB) Still, currently, underdeveloped regions experience a shortage of expert pathologists, impacting the reliability and speed of diagnostic processes. Research on pathological image segmentation, unfortunately, frequently overlooks the diversity of staining procedures and the lack of adequate data, often with disregard for medical considerations. To overcome the difficulties in diagnosing osteosarcoma in developing regions, a novel intelligent diagnostic and treatment scheme for osteosarcoma pathological images, ENMViT, is devised. ENMViT utilizes KIN for the normalization of mismatched images under constrained GPU resources. To address the issue of insufficient data, traditional data enhancement methods, such as cleaning, cropping, mosaic application, Laplacian sharpening, and similar strategies, are employed. For image segmentation, a multi-path semantic segmentation network, encompassing both Transformer and CNN techniques, is utilized. The loss function is modified to account for the spatial domain's edge offset values. Lastly, the noise is refined on the basis of the area spanned by the connected domain. Central South University provided over 2000 osteosarcoma pathological images for experimentation in this paper. The experimental data for this scheme's processing of osteosarcoma pathological images is impressive, showing strong performance in every stage. Segmentation results achieve a notable 94% IoU increase compared to comparative models, demonstrating its importance in the medical field.
Precisely segmenting intracranial aneurysms (IAs) is a critical step in the assessment and management of IAs. Nonetheless, the procedure through which clinicians manually locate and pinpoint IAs is exceptionally laborious. The present study's focus is on developing a deep-learning-based framework, FSTIF-UNet, for isolating IAs in 3D rotational angiography (3D-RA) images that have not undergone reconstruction. Quinine The study at Beijing Tiantan Hospital enrolled 300 patients with IAs, using 3D-RA sequences for their analysis. Inspired by the clinical prowess of radiologists, a Skip-Review attention mechanism is proposed to repeatedly combine the long-term spatiotemporal characteristics of multiple images with the most evident IA features (selected by a pre-detection network). The short-term spatiotemporal features of the 15 three-dimensional radiographic (3D-RA) images, selected from equally-spaced perspectives, are fused together by a Conv-LSTM neural network. Integrating the two modules allows for complete spatiotemporal fusion of the information from the 3D-RA sequence. In network segmentation, FSTIF-UNet yielded a DSC of 0.9109, an IoU of 0.8586, a sensitivity of 0.9314, a Hausdorff distance of 13.58, and an F1-score of 0.8883; each case needed 0.89 seconds for segmentation. The application of FSTIF-UNet yielded a considerable advancement in IA segmentation results relative to standard baseline networks, with an increment in the Dice Similarity Coefficient (DSC) from 0.8486 to 0.8794. The FSTIF-UNet, a novel proposal, provides a practical tool for clinical diagnosis, supporting radiologists.
Among the various complications arising from the sleep-related breathing disorder sleep apnea (SA), pediatric intracranial hypertension, psoriasis, and even sudden death are notable concerns. Therefore, the proactive identification and treatment of SA can effectively mitigate the risk of malignant complications. People employ portable monitoring systems for the purpose of tracking their sleep patterns outside of traditional hospital settings. The aim of this study is to detect SA employing single-lead ECG recordings, which are easily captured using PM technology. Our proposed fusion network, BAFNet, leverages bottleneck attention and includes five crucial elements: RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, global query generation, feature fusion, and the classification process. Fully convolutional networks (FCN) incorporating cross-learning are suggested for acquiring the feature representations of RRI/RPA segments. To regulate the flow of information between RRI and RPA networks, a global query generation method employing bottleneck attention is presented. To optimize the performance of SA detection, a hard sample strategy, specifically incorporating k-means clustering, is implemented. The experimental results demonstrate that BAFNet produces outcomes that are competitive with, and in a number of cases exceed, the present gold standard of SA detection methods. For sleep condition monitoring via home sleep apnea tests (HSAT), BAFNet is likely to prove quite beneficial, with a strong potential. The source code for the Bottleneck-Attention-Based-Fusion-Network-for-Sleep-Apnea-Detection project resides at the specified GitHub URL: https//github.com/Bettycxh/Bottleneck-Attention-Based-Fusion-Network-for-Sleep-Apnea-Detection.
A novel contrastive learning approach for medical images, using labels extracted from clinical data, is presented with a unique strategy for selecting positive and negative sets. The medical field employs a variety of data labels, performing different functions at various stages of the diagnostic and therapeutic process. Clinical labels and biomarker labels exemplify two categories of labeling. 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. Studies within the ophthalmology field have shown correlations between clinical parameters and biomarker structures displayed in optical coherence tomography (OCT) images. Infectious diarrhea We capitalize on this relationship through the use of clinical data as pseudo-labels for our data lacking biomarker labels, thus enabling the selection of positive and negative instances for the training of a fundamental network with a supervised contrastive loss. This approach facilitates a backbone network's learning of a representation space that matches the observed distribution of the clinical data. By applying a cross-entropy loss function to a smaller subset of biomarker-labeled data, we further adjust the network previously trained to directly identify these key disease indicators from OCT scans. This concept is augmented by our method, which utilizes a linear combination of clinical contrastive losses. Within a unique framework, we assess our methods, contrasting them against the most advanced self-supervised techniques, utilizing biomarkers that vary in granularity. We demonstrate a total biomarker detection AUROC improvement of up to 5%.
Medical image processing is essential for the integration of healthcare within the metaverse and the real world. Medical image processing is seeing growing interest in self-supervised denoising techniques that utilize sparse coding approaches, dispensing with the necessity of large-scale training samples. Existing self-supervised methods are plagued by suboptimal performance and low efficiency metrics. This paper proposes the weighted iterative shrinkage thresholding algorithm (WISTA), a novel self-supervised sparse coding method for 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. Differently, to achieve greater denoising proficiency, we construct a deep neural network (DNN) based on the WISTA algorithm, resulting in the WISTA-Net architecture.