[The Generative Artificial Intelligence Revolution in Nursing: A New Chapter in Enhancing Care Quality and Education].

Artificial intelligence (AI) represents a recent major breakthrough in technology development and, in recent years, generative AI has emerged as another trendsetter. The application of generative AI technologies in the healthcare sector has not only opened new possibilities for improving the efficiency of medical diagnoses but also provided healthcare professionals with more-accurate patient monitoring capabilities and optimized care processes. Combining generative AI with nursing expertise holds out the potential of creating a more valuable model of nursing care. The impact of generative AI on the nursing profession poses both challenges and opportunities. By applying appropriate strategies, it is possible to create more advanced and humane nursing values that enhance overall nursing efficiencies and align the nursing field with modern technological advancements. In this article, the development of AI and generative AI is reviewed, and the potential for their application to nursing care is discussed, with the goal of stimulating innovative thinking and new strategies for interdisciplinary collaboration between technology and nursing.

Fall Detection with the Spatial-Temporal Correlation Encoded by a Sequence-to-Sequence Denoised GAN.

Falling is a major cause of personal injury and accidental death worldwide, in particular for the elderly. For aged care, a falling alarm system is highly demanded so that medical aid can be obtained immediately when the fall accidents happen. Previous studies on fall detection lacked practical considerations to deal with real-world situations, including the camera's mounting angle, lighting differences between day and night, and the privacy protection for users. In our experiments, IR-depth images and thermal images were used as the input source for fall detection; as a result, detailed facial information is not captured by the system for privacy reasons, and it is invariant to the lighting conditions. Due to the different occurrence rates between fall accidents and other normal activities, supervised learning approaches may suffer from the problem of data imbalance in the training phase. Accordingly, in this study, anomaly detection is performed using unsupervised learning approaches so that the models were trained only with the normal cases while the fall accident was defined as an anomaly event. The proposed system takes sequential frames as the inputs to predict future frames based on a GAN structure, and it provides (1) multi-subject detection, (2) real-time fall detection triggered by motion, (3) a solution to the situation that subjects were occluded after falling, and (4) a denoising scheme for depth images. The experimental results show that the proposed system achieves the state-of-the-art performance and copes with the real-world cases successfully.

An Efficient and Geometric-Distortion-Free Binary Robust Local Feature.

An efficient and geometric-distortion-free approach, namely the fast binary robust local feature (FBRLF), is proposed. The FBRLF searches the stable features from an image with the proposed multiscale adaptive and generic corner detection based on the accelerated segment test (MAGAST) to yield an optimum threshold value based on adaptive and generic corner detection based on the accelerated segment test (AGAST). To overcome the problem of image noise, the Gaussian template is applied, which is efficiently boosted by the adoption of an integral image. The feature matching is conducted by incorporating the voting mechanism and lookup table method to achieve a high accuracy with low computational complexity. The experimental results clearly demonstrate the superiority of the proposed method compared with the former schemes regarding local stable feature performance and processing efficiency.

A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.

Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. Developing an automatically computer-aided algorithm for glioma subtype classification is challenging due to many factors. One of the difficulties is the label constraint. Specifically, each case is simply labeled the glioma subtype without precise annotations of lesion regions information. In this paper, we propose a novel hybrid fully convolutional neural network (CNN)-based method for glioma subtype classification using both whole slide imaging (WSI) and multiparametric magnetic resonance imagings (mpMRIs). It is comprised of two methods: a WSI-based method and a mpMRIs-based method. For the WSI-based method, we categorize the glioma subtype using a 2D CNN on WSIs. To overcome the label constraint issue, we extract the truly representative patches for the glioma subtype classification in a weakly supervised fashion. For the mpMRIs-based method, we develop a 3D CNN-based method by analyzing the mpMRIs. The mpMRIs-based method consists of brain tumor segmentation and classification. Finally, to enhance the robustness of the predictions, we fuse the WSI-based and mpMRIs-based results guided by a confidence index. The experimental results on the validation dataset in the competition of CPM-RadPath 2020 show the comprehensive judgments from both two modalities can achieve better performance than the ones by solely using WSI or mpMRIs. Furthermore, our result using the proposed method ranks the third place in the CPM-RadPath 2020 in the testing phase. The proposed method demonstrates a competitive performance, which is creditable to the success of weakly supervised approach and the strategy of label agreement from multi-modality data.

Deep Concatenated Residual Networks for Improving Quality of Halftoning-Based BTC Decoded Image.

This paper presents a simple technique for improving the quality of the halftoning-based block truncation coding (H-BTC) decoded image. The H-BTC is an image compression technique inspired from typical block truncation coding (BTC). The H-BTC yields a better decoded image compared to that of the classical BTC scheme under human visual observation. However, the impulsive noise commonly appears on the H-BTC decoded image. It induces an unpleasant feeling while one observes this decoded image. Thus, the proposed method presented in this paper aims to suppress the occurring impulsive noise by exploiting a deep learning approach. This process can be regarded as an ill-posed inverse imaging problem, in which the solution candidates of a given problem can be extremely huge and undetermined. The proposed method utilizes the convolutional neural networks (CNN) and residual learning frameworks to solve the aforementioned problem. These frameworks effectively reduce the impulsive noise occurrence, and at the same time, it improves the quality of H-BTC decoded images. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements.

Improved block truncation coding based on the void-and-cluster dithering approach.

Block Truncation Coding (BTC) is an efficient technology for image compression. An improved BTC algorithm, namely Ordered Dither Block Truncation Coding (ODBTC), is presented in this study. In order to provide better image quality, the void-and-cluster halftoning is combined with the BTC. The ODBTC results show that the image quality is improved when it is operated in high coding gain applications. Another feature of the ODBTC is the dither array Look Up Table (LUT), which significantly reduces the complexity compared to the BTC.

Improved dot diffusion by diffused matrix and class matrix co-optimization.

Dot diffusion is an efficient approach which utilizes concepts of block-wise and parallel-oriented processing to generate halftones. However, the block-wise nature of processing reduces image quality much more significantly as compared to error diffusion. In this work, four types of filters with various sizes are employed in co-optimization procedures with class matrices of size 8 n 8 and 16 x 16 to improve the image quality. The optimal diffused weighting and area are determined through simulations. Many well-known halftoning methods, some of which includes direct binary search (DBS), error diffusion, ordered dithering, and prior dot diffusion methods, are also included for comparisons. Experimental results show that the proposed dot diffusion achieved quality close to some forms of error diffusion, and additionally, superior to the well-known Jarvis and Stucki error diffusion and Mese's dot diffusion. Moreover, the inherent parallel processing advantage of dot diffusion is preserved, allowing us to reap higher executing efficiency than both DBS and error diffusion.

Multi-Person Pose Estimation via Multi-Layer Fractal Network and Joints Kinship Pattern.

We propose an effective method to boost the accuracy of multi-person pose estimation in images. Initially, the three-layer fractal network was constructed to regress multi-person joints location heatmap that can help to enhance an image region with receptive field and capture more joints local-contextual feature information, thereby producing keypoints heatmap intermediate prediction to optimize human body joints regression results. Subsequently, the hierarchical bi-directional inference algorithm was proposed to calculate the degree of relatedness (call it Kinship) for adjacent joints, and it combines the Kinship between adjacent joints with the spatial constraints, which we refer to as joints kinship pattern matching mechanism, to determine the best matched joints pair. We iterate the above-mentioned joints matching process layer by layer until all joints are assigned to a corresponding individual. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art schemes and achieves about 1% and 0.6% increase in mAP on MPII multi-person subset and MSCOCO 2016 keypoints challenge.

An Efficient Fusion-Based Defogging.

Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. To overcome this problem, conventional approaches focus mainly on the enhancement of the overall image contrast. However, because of the unspecified light-source distribution or unsuitable mathematical constraints of the cost functions, it is often difficult to achieve quality results. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improve the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The systematic assessments show that this approach is capable of achieving superior defogging and dehazing performance, compared with superior defogging and dehazing performance, compared with the state-of-the-art methods, both quantitatively and qualitatively.

High-capacity data hiding in halftone images using minimal-error bit searching and least-mean square filter.

In this paper, a high-capacity data hiding is proposed for embedding a large amount of information into halftone images. The embedded watermark can be distributed into several error-diffused images with the proposed minimal-error bit-searching technique (MEBS). The method can also be generalized to self-decoding mode with dot diffusion or color halftone images. From the experiments, the embedded capacity from 33% up to 50% and good quality results are achieved. Furthermore, the proposed MEBS method is also extended for robust watermarking against the degradation from printing-and-scanning and several kinds of distortions. Finally, a least-mean square-based halftoning is developed to produce an edge-enhanced halftone image, and the technique also cooperates with MEBS for all the applications described above, including high-capacity data hiding with secret sharing or self-decoding mode, as well as robust watermarking. The results prove much sharper than the error diffusion or dot diffusion methods.

Clustered-Dot Screen Design for Digital Multitoning.

Digital multitoning is an extension of halftoning for rendering more than two tones at each pixel for higher image quality. Although a lot of effort has been put in generating dispersed dots previously, the blue-noise feature can hardly be achieved for those printers utilizing the electrophotography (EP) process to avoid the physically unstable isolated dots. To overcome this issue, Chandu et al. proposed a screening method for yielding green-noise dot clusters, yet noisy multitone texture was accompanied. This degrades the visual quality and the stability of tone rendering. In this paper, a significantly improved homogeneity of clustered dots can be achieved by the proposed screening method based upon the new inter-iterative clustered-dot direct multi-bit search algorithm. Compared with the former approaches, the inter-iteration design leads to less error by the updated initial multitone patterns. As demonstrated in the experimental results, both of the high homogenous multitone texture and less noisy perception at all absorptance levels are offered in contrast to the former Chandu et al.'s results. The high-quality output proves it as a very competitive candidate for EP printers, e.g., laser printers.

Dot-Diffused Halftoning With Improved Homogeneity.

Compared with the error diffusion, dot diffusion provides an additional pixel-level parallelism for digital halftoning. However, even though its periodic and blocking artifacts had been eased by the previous works, it was still far from satisfactory in terms of the blue noise spectrum perspective. In this paper, we strengthen the relation among the pixel locations of the same processing order by an iterative halftoning method, and the results demonstrate a significant improvement. Moreover, a new approach of deriving the averaged power spectrum density is proposed to avoid the regular sampling of the well-known Bartlett's procedure which inaccurately presents the halftone periodicity of certain halftoning techniques with parallelism. As a result, the proposed dot diffusion is substantially superior to the state-of-the-art parallel halftoning methods in terms of visual quality and artifact-free property, and competitive runtime to the theoretical fastest ordered dithering is offered simultaneously.

Content-based image retrieval using features extracted from halftoning-based block truncation coding.

This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.

Progressive halftone watermarking using multilayer table lookup strategy.

In this paper, a halftoning-based multilayer watermarking of low computational complexity is proposed. An additional data-hiding technique is also employed to embed multiple watermarks into the watermark to be embedded to improve the security and embedding capacity. At the encoder, the efficient direct binary search method is employed to generate 256 reference tables to ensure the output is in halftone format. Subsequently, watermarks are embedded by a set of optimized compressed tables with various textural angles for table lookup. At the decoder, the least mean square metric is considered to increase the differences among those generated phenotypes of the embedding angles and reduce the required number of dimensions for each angle. Finally, the naïve Bayes classifier is employed to collect the possibilities of multilayer information for classifying the associated angles to extract the embedded watermarks. These decoded watermarks can be further overlapped for retrieving the additional hidden-layer watermarks. Experimental results show that the proposed method requires only 8.4 ms for embedding a watermark into an image of size 512×512 , under the 32-bit Windows 7 platform running on 4GB RAM, Intel core i7 Sandy Bridge with 4GB RAM and IDE Visual Studio 2010. Finally, only 2 MB is required to store the proposed compressed reference table.

Tone-Replacement Error Diffusion for Multitoning.

Error diffusion is an efficient halftone method for mainly being applied on printers. The promising high image quality and processing efficiency endorse it as a popular and competitive candidate in halftoning and multitoning applications. The multitoning is an extension of halftoning, adopting more than two-tone levels for the improvement of the similarity between an original image and the converted image. Yet, the banding effect, indicating the areas with discontinuous tone level, disturbs the visual perception, and thus seriously degrades image quality. To solve the banding effect, the tone-replacement strategy is proposed in this paper. As documented in the experimental results, excellent tone-similarity as that of the original image and promising reconstructed dot-distribution can be provided simultaneously. Comparing with the former banding-free methods, the apparent improvements/features suggest that the proposed method can be a very competitive candidate for multitoning applications.

A unified data embedding and scrambling method.

Conventionally, data embedding techniques aim at maintaining high-output image quality so that the difference between the original and the embedded images is imperceptible to the naked eye. Recently, as a new trend, some researchers exploited reversible data embedding techniques to deliberately degrade image quality to a desirable level of distortion. In this paper, a unified data embedding-scrambling technique called UES is proposed to achieve two objectives simultaneously, namely, high payload and adaptive scalable quality degradation. First, a pixel intensity value prediction method called checkerboard-based prediction is proposed to accurately predict 75% of the pixels in the image based on the information obtained from 25% of the image. Then, the locations of the predicted pixels are vacated to embed information while degrading the image quality. Given a desirable quality (quantified in SSIM) for the output image, UES guides the embedding-scrambling algorithm to handle the exact number of pixels, i.e., the perceptual quality of the embedded-scrambled image can be controlled. In addition, the prediction errors are stored at a predetermined precision using the structure side information to perfectly reconstruct or approximate the original image. In particular, given a desirable SSIM value, the precision of the stored prediction errors can be adjusted to control the perceptual quality of the reconstructed image. Experimental results confirmed that UES is able to perfectly reconstruct or approximate the original image with SSIM value > 0.99 after completely degrading its perceptual quality while embedding at 7.001 bpp on average.

New class tiling design for dot-diffused halftoning.

In this paper, a new class tiling designed dot diffusion along with the optimized class matrix and diffused matrix are proposed. The result of this method presents a nearly periodic-free halftone when compared to the former schemes. Formerly, the class matrix of the dot diffusion is duplicated and orthogonally tiled to fulfill the entire image for further thresholding and quantized-error diffusion, which accompanies subsequent periodic artifacts. In our observation, this artifact can be solved by manipulating the class tiling with comprising rotation, transpose, and alternatively shifting of the class matrices. As documented in the experimental results, the proposed dot diffusion has been compared with the former halftoning methods with parallelism in terms of image quality, processing efficiency, periodicity, and memory consumption; the proposed dot diffusion exhibits as a very competitive candidate in the printing/display market.

Efficient halftoning based on multiple look-up tables.

Look-up table (LUT) halftoning is an efficient way to construct halftone images and approximately simulate the dot distribution of the learned halftone image set. In this paper, a general mechanism named multiple look-up table (MLUT) halftoning is proposed to generate the halftones of direct binary search (DBS), whereas the high efficient characteristic of the LUT is still preserved. In the MLUT, the standard deviation is adopted as an important feature to classify various tables. In addition, the proposed quick standard deviation evaluation is employed to yield an extremely low computational complexity in calculating the standard deviation. In the parameter optimization, the autocorrelation is adopted because it can fully characterize the periodicity of dot distribution. Experimental results demonstrate that the dot distribution generated by the proposed method approximates to that of the DBS, which enables the proposed scheme as a very competitive candidate in the copying and printing industry.

[Activation of NOD2 signalling pathway stimulates the function of human dendritic cells loaded with leukemia cell lysates].

The purpose of this study was to explore the effect of NOD2 signalling pathway activated by muramyl dipeptide (MDP) on the immunomodulation effect of human monocyte-derived dendritic cells (DC) loaded with leukemia cell lysates. Peripheral blood mononuclear cells (PBMNC) were isolated by density gradient centrifugation, These cells were cultured with three cytokines for 7 days to induce their maturation. On the 5th day, cells were loaded with leukemia cell HL-60 lysates. NOD2 expression was detected by RT-PCR and Western blot. The phenotype of DC were analyzed by flow cytometry, and ELISA was used to assay levels of IL-12 (p40) . The results showed that MDP could trigger NOD2 mRNA and protein expression in different groups of DC, especially in sensitized DC+MDP group, which was significantly higher than that in the DC+MDP group and sensitized DC without MDP stimulation, the difference was statistically significant (P < 0.05). Besides, the expression of surface molecules (HLA-DR, CD80, CD83, CD86, CD40) in the group of DC loaded with leukemia cell lysate and stimulated by MDP (sensitized DC+MDP) reached the highest level, followed by the group of DC loaded with leukemia cell lysate without MDP and DC only stimulated by MDP, non-treated DC were the lowest (P < 0.05). Similarly, compared with untreated unstimulated DC, after loading with HL-60 lysates or only stimulating with MDP, the secretion of IL-12p40 increased, but IL-12p40 level (573.86 ± 32.09 pg/ml) in DC+MDP group was higher than that in group of sensitized DC (365.03 ± 28.86 pg/ml) (P < 0.05), and it in sensitized DC+MDP group reached the highest (898.30 ± 61.08) pg/ml, compared to other groups (P < 0.05). It is concluded that MDP can significantly enhance the NOD2 mRNA and protein expression in sensitized DC, promote the expression of HLA-DR, synergistic costimulatory molecules and adhesion molecules of DC, at the same time, MDP can increase secretion of inflammatory factors IL-12p40. This study will provide a new ideas for DC application in leukemia immunotherapy.

High capacity data hiding for error-diffused block truncation coding.

Block truncation coding (BTC) is an efficient compression technique with extremely low computational complexity. However, the blocking and false contour effects are two major deficiencies in BTC which cause severe perceptual artifacts. The former scheme, error-diffused BTC (EDBTC), can significantly improve the above issues through the visual low-pass compensation on the bitmap, which thus widens its possible application market, yet the corresponding security issue may limit its value. In this paper, a method namely complementary hiding EDBTC is developed to cope the above issue. This paper is managed by firstly discussing when a single watermark is embedded, and then multiple watermarks are employed to test the limitation of the proposed scheme. Herein, an adaptive external bias factor is employed to control the watermark embedding, and this factor also affects the image quality and robustness simultaneously. Experimental results demonstrate that the proposed method only requires an extremely small external bias factor to carry watermarks, which enables a high capacity scenario without significantly damaging image quality.