Reviewerly/exHarmony huggingface dataset:
Multi-Level Swin Transformer Enabled Automatic Segmentation and Classification of Breast Metastases

Detection of metastatic breast cancer lesions is a challenging task in breast cancer treatment. The recent advancements in deep learning gained attention owing to its robustness, particularly in addressing automated segmentation and classification issues in medical images. In this paper, we proposed a modified Swin Transformer model (mST) integrated with a novel Multi-Level Adaptive Feature Fusion (MLAFF) Module. We constructed a modified Swin Transformer network comprising of a Local Transferable MSA (LT-MSA) and a Global Transferable MSA (GT-MSA) in addition to a Feed Forward Network (FFN). Our novel Multi-Level Adaptive Feature Fusion (MLAFF) module iteratively combines the features throughout multiple transformers. We utilized a pre-trained deep learning model U-Net and trained it on mammography utilizing Transfer Learning for automated segmentation. The proposed method, mST-MLAFF, is used for breast cancer classification into normal, benign, and malignant classes. Our model outperformed comparison methods based on U-Net and Swin Transformer in breast metastatic lesion segmentation on the seven benchmark datasets, namely INBreast, DDSM, MIAS, CBIS-DDSM, MIMBCD-UI, KAU-BCMD, and Mammographic Masses. Our model achieved 98% Dice-Similarity coefficient (DSC) for segmentation and an average of 94.5% accuracy for classification, whereas U-Net based model achieved 92% DSC and Swin Transformer achieved 93% DSC. Extensive performance evaluation of our model on benchmark datasets shows the potential of our model for breast cancer classification.Clinical relevance- This research work is focused on assisting the radiologist in the early detection and classification of breast cancer. A single mammography image is analyzed in less than a minute for automated segmentation and classification into malignant and benign classes.
Consumer Segments in Blockchain Technology Adoption

Abstract Recent research has highlighted the adoption of blockchain technology (BCT) and the increasing recognition of its multiple capabilities for users. The main objective of this paper is to delineate different consumer segments based on their BCT beliefs and attitudes. Using a survey method, 934 questionnaires were collected and subjected to hierarchical and K-Means cluster analysis and contingency analysis. The results reveal four identifiable user segments with respect to BCT adoption: Innovators (enthusiastic about BCT), Cautious (careful about BCT), Sceptics (doubtful about BCT), and Suspicious (very distrustful about BCT). These results provide valuable insights into understanding behavioral intentions and serve as a basis for future research.
Enhanced Communication of Tumor Margins Using 3D Scanning and Mapping

After oncologic resection of malignant tumors, specimens are sent to pathology for processing to determine the surgical margin status. These results are communicated in the form of a written pathology report. The current standard-of-care pathology report provides a written description of the specimen and the sites of margin sampling without any visual representation of the resected tissue. The specimen itself is typically destroyed during sectioning and analysis. This often leads to challenging communication between pathologists and surgeons when the final pathology report is confirmed. Furthermore, surgeons and pathologists are the only members of the multidisciplinary cancer care team to visualize the resected cancer specimen. We have developed a 3D scanning and specimen mapping protocol to address this unmet need. Computer-aided design (CAD) software is used to annotate the virtual specimen clearly showing sites of inking and margin sampling. This map can be utilized by various members of the multidisciplinary cancer care team.
Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease

Abstract Objectives Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions. Materials and methods PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining. Results Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally. Discussion Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site. Conclusion PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches.
An improved human activity recognition technique based on convolutional neural network

A convolutional neural network (CNN) is an important and widely utilized part of the artificial neural network (ANN) for computer vision, mostly used in the pattern recognition system. The most important applications of CNN are medical image analysis, image classification, object recognition from videos, recommender systems, financial time series analysis, natural language processing, and human-computer interfaces. However, after the technological advancement in the power of computing ability and the emergence of huge quantities of labeled data provided through enhanced algorithms, nowadays, CNN is widely used in almost every area of study. One of the main uses of wearable technology and CNN within medical surveillance is human activity recognition (HAR), which must require constant tracking of everyday activities. This paper provides a comprehensive study of the application of CNNs in the classification of HAR tasks. We describe their enhancement, from their antecedents up to the current state-of-the-art systems of deep learning (DL). We have provided a comprehensive working principle of CNN for HAR tasks, and a CNN-based model is presented to perform the classification of human activities. The proposed technique interprets data from sensor sequences of inputs by using a multi-layered CNN that gathers temporal and spatial data related to human activities. The publicly available WISDM dataset for HAR has been used to perform this study. This proposed study uses the two-dimensional CNN approach to make a model for the classification of different human activities. A recent version of Python software has been used to perform the study. The rate of accuracy for HAR through the proposed model in this experiment is 97.20%, which is better than the previously estimated state-of-the-art technique. The findings of the study imply that using DL methods for activity recognition might greatly increase accuracy and increase the range of applications where HAR can be used successfully. We have also described the future research trends in the field of HAR in this article.
CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

Abstract Objectives Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. Materials and methods We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. Results The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of &amp;gt;1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. Discussion and conclusion The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

Background and Objectives: This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. Materials and Methods: We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient. Results: The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar. Conclusion: The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.
Explaining through Transformer Input Sampling

Vision Transformers are becoming more and more the preferred solution to many computer vision problems, which has motivated the development of dedicated explainability methods. Among them, perturbation-based methods offer an elegant way to build saliency maps by analyzing how perturbations of the input image affect the network prediction. However, those methods suffer from the drawback of introducing outlier image features that might mislead the explainability process, e.g. by affecting the output classes independently of the initial image content. To overcome this issue, this paper introduces Transformer Input Sampling (TIS), a perturbation-based explainability method for Vision Transformers, which computes a saliency map based on perturbations induced by a sampling of the input tokens. TIS utilizes the natural property of Transformers which permits a variable input number of tokens, thereby preventing the use of replacement values to generate perturbations. Using standard models such as ViT and DeiT for benchmarking, TIS demonstrates superior performance on several metrics including Insertion, Deletion, and Pointing Game compared to state-of-the-art explainability methods for Transformers. The code for TIS is publicly available at https://github.com/aenglebert/Transformer_Input_Sampling.
On the Value of a Scientific Document. Part 1

The author considers the terminological confusion associated with designations of properties of scientific documents represented by their citation rate. Once again, we argue in favor of the statement that the citedness of research documents directly represents their use, which in turn represents their value. The need to continue the relevant polemic and strengthen the argumentation is caused by the fact that many experts either do not associate citedness with value or are indifferent to the issue of properties of documents represented by citation rate. Also, many bibliometricians confuse value of documents with their quality. Moreover, there is a point of view according to which the use is only one of the factors that cause citations to scientific documents; while just addressing to documents is considered, on the contrary, as an undoubted evidence of their use – though in this case it remains unknown whether the full text of the retrieved document has been at least briefly reviewed by the user. The article gives critical consideration to these issues. The opinion is expressed that a certain misunderstanding of the concept “use of a document” is due to its involuntary confusion with the traditional library concept “use of a library stock”. It is also shown that taking the philosophical notion of value for consideration can add even more confusion to the terminology used to denote the properties of a scientific document that are represented by its citation rate par excellence. Some definitions of the concept “document’s value” that circulate in contemporary library science are critically analyzed. Accordingly, the author makes an attempt to clarify the concepts and content of the terms “value of a scientific document” and “use of a scientific document”. Specifically, the present first part of this work contains mainly examples and criticism of the terminological confusion about the designation of the properties of a scientific document represented by its citedness and use. They are considered through the prism of the unspecified concepts of value and quality of a research document. The author used them before completing his work on the second part of this article. It will elaborate and specify the concept of value of a scientific document.
Anomaly detection methods in sample datasets when managing processes in systems by the state

The current information software does not allow solving the problems of detecting outliers in data samples and time series with a sufficiently high level of reliability. Therefore, this work is devoted to the choice of metrics for assessing the correctness of detecting outliers, as well as the best mathematical models and methods for solving the problem of detecting outliers in test samples when managing processes in systems by state. Mathematical models and methods for detecting outliers (anomalous values) and Python-based software tools such as scikit-learn, Tensorflow, NumPy, Pandas and others have been used. The results of our work are the overview of the metrics used to assess the effectiveness of mathematical models and methods for detecting outliers; the overview of traditional and deep learning techniques of detecting outliers; the results of researching the efficiency and quality of mathematical models and methods for detecting outliers using 12 datasets; the conclusions about the best metric and the best mathematical models and methods for solving the problem of detecting outliers in test samples when managing processes in systems by state. The selected methods are mainly used for monitoring the level of anomalous values in various datasets when managing processes in systems by state, which makes these methods universal.
Intelligent Resource Allocation and Optimization for Industrial Robotics Using AI and Blockchain

This chapter focuses on the application of intelligent resource allocation and optimization techniques for industrial robotics systems using the synergistic integration of artificial intelligence (AI) and blockchain technologies. Efficient resource allocation is crucial for maximizing the performance and productivity of industrial robotics, and AI-based approaches offer the ability to dynamically allocate resources based on real-time data and system requirements. Additionally, blockchain technology provides a decentralized and secure platform for recording and verifying resource allocation transactions, ensuring transparency and trust in the allocation process. The chapter explores various AI algorithms and models that can be employed for resource allocation and optimization in industrial robotics, including machine learning, evolutionary algorithms, and reinforcement learning. Furthermore, the chapter investigates how blockchain technology can enhance resource allocation and optimization by providing a distributed ledger for recording and verifying resource transactions.
Cascaded liquid crystal holography for optical encryption

Cascaded holography coupled with the secret-sharing scheme has recently gained considerable attention due to its enhanced information processing and encryption capabilities. Here, we propose a new holographic iterative algorithm and present the implementation of cascaded liquid crystal (LC) holography for optical encryption. Each LC layer acts as the secret key and can generate a distinct holographic image. By cascading two LC elements, a new holographic image is formed. Additionally, we showcase the dynamic optical encryption achieved by electrically switching LCs with combined electric keys. This work may offer promising applications in optical cryptography, all-optical computing, and data storage.
ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis

Whole slide image (WSI) analysis has become increasingly important in the medical imaging community, enabling automated and objective diagnosis, prognosis, and therapeutic-response prediction. However, in clinical practice, the ever-evolving environment hamper the utility of WSI analysis models. In this paper, we propose the FIRST continual learning framework for WSI analysis, named ConSlide, to tackle the challenges of enormous image size, utilization of hierarchical structure, and catastrophic forgetting by progressive model updating on multiple sequential datasets. Our framework contains three key components. The Hierarchical Interaction Transformer (HIT) is proposed to model and utilize the hierarchical structural knowledge of WSI. The Breakup-Reorganize (BuRo) rehearsal method is developed for WSI data replay with efficient region storing buffer and WSI reorganizing operation. The asynchronous updating mechanism is devised to encourage the network to learn generic and specific knowledge respectively during the replay stage, based on a nested cross-scale similarity learning (CSSL) module. We evaluated the proposed ConSlide on four public WSI datasets from TCGA projects. It performs best over other state-of-the-art methods with a fair WSI-based continual learning setting and achieves a better trade-off of the overall performance and forgetting on previous tasks.
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks

In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural Networks (DNNs) that addresses the challenge of optimizing the output distortion minimization while adhering to a target pruning ratio constraint. Our approach takes into account the collective influence of all layers to design a layer-adaptive pruning scheme. We discover and utilize a very important additivity property of output distortion caused by pruning weights on multiple layers. This property enables us to formulate the pruning as a combinatorial optimization problem and efficiently solve it through dynamic programming. By decomposing the problem into sub-problems, we achieve linear time complexity, making our optimization algorithm fast and feasible to run on CPUs. Our extensive experiments demonstrate the superiority of our approach over existing methods on the ImageNet and CIFAR-10 datasets. On CIFAR-10, our method achieves remarkable improvements, outperforming others by up to 1.0% for ResNet-32, 0.5% for VGG-16, and 0.7% for DenseNet-121 in terms of top-1 accuracy. On ImageNet, we achieve up to 4.7% and 4.6% higher top-1 accuracy compared to other methods for VGG-16 and ResNet-50, respectively. These results highlight the effectiveness and practicality of our approach for enhancing DNN performance through layer-adaptive weight pruning. Code will be available on https://github.com/Akimoto-Cris/RD_VIT_PRUNE.
Mixed Neural Voxels for Fast Multi-view Video Synthesis

Synthesizing high-fidelity videos from real-world multi-view input is challenging due to the complexities of real-world environments and high-dynamic movements. Previous works based on neural radiance fields have demonstrated high-quality reconstructions of dynamic scenes. However, training such models on real-world scenes is time-consuming, usually taking days or weeks. In this paper, we present a novel method named MixVoxels to efficiently represent dynamic scenes, enabling fast training and rendering speed. The proposed MixVoxels represents the 4D dynamic scenes as a mixture of static and dynamic voxels and processes them with different networks. In this way, the computation of the required modalities for static voxels can be processed by a lightweight model, which essentially reduces the amount of computation as many daily dynamic scenes are dominated by static backgrounds. To distinguish the two kinds of voxels, we propose a novel variation field to estimate the temporal variance of each voxel. For the dynamic representations, we design an inner product time query method to efficiently query multiple time steps, which is essential to recover the high-dynamic movements. As a result, with 15 minutes of training for dynamic scenes with inputs of 300-frame videos, MixVoxels achieves better PSNR than previous methods. For rendering, MixVoxels can render a novel view video with 1K resolution at 37 fps. Codes and trained models are available at https://github.com/fengres/mixvoxels.