Reviewerly/exHarmony huggingface dataset:
Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced Data Classification

Authors:
raw_author_name: Rishav Pramanik
raw_affiliation_string: Department of Computer Science and Engineering, Jadavpur University, Kolkata, WB, India
raw_author_name: Ram Sarkar
raw_affiliation_string: Department of Computer Science and Engineering, Jadavpur University, Kolkata, WB, India

Abstract: In supervised learning algorithms, the class imbalance problem often leads to generate results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal aspect of separating the overlapping classes. This is the reason why most of these methods are prone to overfit on the training data. To this end, we propose a novel loss function, namely Margin-Aware Adaptive-Weighted (MAAW) loss. Here, we first use the large margin softmax to leverage intra-class compactness and inter-class separability. Further to learn an unbiased representation of the classes, we put forward a dynamically weighted loss for imbalanced data classification. This weight dynamically adapts on every minibatch based on the inverse class frequencies. In addition, it takes care of the hard-to-train samples by using the confidence scores to learn discriminative hidden representations of the data. The overall framework is found to be effective when evaluated on two widely used datasets, namely CIFAR-10 and Fashion-MNIST. Additional experiments on HAM10000 and APTOS-2019 BD datasets prove the robustness of our methodology. The source codes of the proposed methodology is provided under the GitHub repository <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/rishavpramanik/MAAW</uri>
Unsupervised Domain Adaptation for Medical Image Segmentation Using Transformer With Meta Attention

Authors:
raw_author_name: Albert C. S. Chung
raw_affiliation_string: Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

Abstract: Image segmentation is essential to medical image analysis as it provides the labeled regions of interest for the subsequent diagnosis and treatment. However, fully-supervised segmentation methods require high-quality annotations produced by experts, which is laborious and expensive. In addition, when performing segmentation on another unlabeled image modality, the segmentation performance will be adversely affected due to the domain shift. Unsupervised domain adaptation (UDA) is an effective way to tackle these problems, but the performance of the existing methods is still desired to improve. Also, despite the effectiveness of recent Transformer-based methods in medical image segmentation, the adaptability of Transformers is rarely investigated. In this paper, we present a novel UDA framework using a Transformer for building a cross-modality segmentation method with the advantages of learning long-range dependencies and transferring attentive information. To fully utilize the attention learned by the Transformer in UDA, we propose Meta Attention (MA) and use it to perform a fully attention-based alignment scheme, which can learn the hierarchical consistencies of attention and transfer more discriminative information between two modalities. We have conducted extensive experiments on cross-modality segmentation using three datasets, including a whole heart segmentation dataset (MMWHS), an abdominal organ segmentation dataset, and a brain tumor segmentation dataset. The promising results show that our method can significantly improve performance compared with the state-of-the-art UDA methods.
Sensor Data Protection Through Integration of Blockchain and Camouflaged Encryption in Cyber-Physical Manufacturing Systems

Authors:
raw_author_name: Zhangyue Shi
raw_affiliation_string: Oklahoma State University The School of Industrial Engineering & Management, , Stillwater, OK 74078
raw_author_name: Wenmeng Tian
raw_affiliation_string: Mississippi State University Department of Industrial and Systems Engineering, , Mississippi State, MS 39762
raw_author_name: Chen Kan
raw_affiliation_string: The University of Texas at Arlington Department of Industrial, Manufacturing, and Systems Engineering, , Arlington, TX 76019
raw_author_name: Chenang Liu
raw_affiliation_string: Oklahoma State University The School of Industrial Engineering & Management, , Stillwater, OK 74078

Abstract: Abstract The advancement of sensing technology enables efficient data collection from manufacturing systems for monitoring and control. Furthermore, with the rapid development of the Internet of Things (IoT) and information technologies, more and more manufacturing systems become cyber-enabled, facilitating real-time data sharing and information exchange, which significantly improves the flexibility and efficiency of manufacturing systems. However, the cyber-enabled environment may pose the collected sensor data with high risks of cyber-physical attacks during the data and information sharing. Specifically, cyber-physical attacks could target the manufacturing process and/or the data transmission process to maliciously tamper the sensor data, resulting in false alarms or failures in anomaly detection in monitoring. In addition, cyber-physical attacks may also enable illegal data access without authorization and cause the leakage of key product/process information. Therefore, it becomes critical to develop an effective approach to protect data from these attacks so that the cyber-physical security of the manufacturing systems can be assured in the cyber-enabled environment. To achieve this goal, this paper proposes an integrative blockchain-enabled data protection method by leveraging camouflaged asymmetry encryption. A real-world case study that protects the cyber-physical security of collected sensor data in additive manufacturing is presented to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time (less than 0.05 ms), and the risk of unauthorized data access is significantly reduced as well.
sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin

Authors:
raw_author_name: Yao Guo
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Jiayan Liu
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Yonglin Wu
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Xinyu Jiang
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Yalin Wang
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Long Meng
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Xiangyu Liu
raw_affiliation_string: College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, P. R. China
raw_author_name: Feng Shu
raw_affiliation_string: Academy for Engineering and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Chenyun Dai
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China
raw_author_name: Wei Chen
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, P. R. China

Abstract: Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.
Deep Kernel Principal Component Analysis for multi-level feature learning

Authors:
raw_author_name: Qinghua Tao
raw_affiliation_string: Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
raw_author_name: Panagiotis Patrinos
raw_affiliation_string: Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
raw_author_name: Johan A.K. Suykens
raw_affiliation_string: Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium

Abstract: Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking. Here we develop a deep kernel PCA methodology (DKPCA) to extract multiple levels of the most informative components of the data. Our scheme can effectively identify new hierarchical variables, called deep principal components, capturing the main characteristics of high-dimensional data through a simple and interpretable numerical optimization. We couple the principal components of multiple KPCA levels, theoretically showing that DKPCA creates both forward and backward dependency across levels, which has not been explored in kernel methods and yet is crucial to extract more informative features. Various experimental evaluations on multiple data types show that DKPCA finds more efficient and disentangled representations with higher explained variance in fewer principal components, compared to the shallow KPCA. We demonstrate that our method allows for effective hierarchical data exploration, with the ability to separate the key generative factors of the input data both for large datasets and when few training samples are available. Overall, DKPCA can facilitate the extraction of useful patterns from high-dimensional data by learning more informative features organized in different levels, giving diversified aspects to explore the variation factors in the data, while maintaining a simple mathematical formulation.
Double Transformer Super-Resolution for Breast Cancer ADC Images

Authors:
raw_author_name: Ying Yang
raw_affiliation_string: Department of Mathematics, The Chinese University of Hong Kong, Shatin, Hong Kong
raw_author_name: Tao Xiang
raw_affiliation_string: College of Computer Science, Chongqing University, Chongqing, China
raw_author_name: Xiao Lv
raw_affiliation_string: College of Computer Science, Chongqing University, Chongqing, China
raw_author_name: Lok Ming Lui
raw_affiliation_string: Department of Mathematics, The Chinese University of Hong Kong, Shatin, Hong Kong
raw_author_name: Tieyong Zeng
raw_affiliation_string: Department of Mathematics, The Chinese University of Hong Kong, Shatin, Hong Kong

Abstract: Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.
WBC YOLO-ViT: 2 Way - 2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer

Authors:
raw_author_name: Jiyoung Woo
raw_affiliation_string: Department of ICT Convergence, Soonchunhyang University, Asan, South Korea

Abstract: Accurate detection and classification of white blood cells, otherwise known as leukocytes, play a critical role in diagnosing and monitoring various illnesses. However, conventional methods, such as manual classification by trained professionals, must be revised in terms of accuracy, efficiency, and potential bias. Moreover, applying deep learning techniques to detect and classify white blood cells using microscopic images is challenging owing to limited data, resolution noise, irregular shapes, and varying colors from different sources. This study presents a novel approach integrating object detection and classification for numerous type-white blood cell. We designed a 2-way approach to use two types of images: WBC and nucleus. YOLO (fast object detection) and ViT (powerful image representation capabilities) are effectively integrated into 16 classes. The proposed model demonstrates an exceptional 96.449% accuracy rate in classification.
Automatic patient-level recognition of four <i>Plasmodium</i> species on thin blood smear by a real-time detection transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation

Authors:
raw_author_name: Baptiste Routier
raw_affiliation_string: Laboratory of Parasitology-Mycology, EA7510 ESCAPE, University Hospital of Rouen, University of Rouen Normandie, Normandie, France

Abstract: Malaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. The algorithm was trained and validated on a data set consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test data set of 4,508 images from 170 smears corresponding to 358,825 labels coming from six French university hospitals. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among Plasmodium-positive smears, the global accuracy was 82.7% (91/110) with a recall of 90% (38/42), 81.8% (18/22), and 76.1% (35/46) for P. falciparum, P. malariae, and P. ovale/vivax, respectively. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas lacking microscopy experts.IMPORTANCEMalaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. Performances were calculated with a test data set of 4,508 images from 170 smears coming from six French university hospitals. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices and could be suitable for deployment in low-resource setting areas.
Counterfactual formulation of patient-specific root causes of disease

Authors:
raw_author_name: Eric V. Strobl
raw_affiliation_string: Department of Psychiatry & Behavioral Sciences, 1601 23rd Avenue South, Nashville, 37232, TN, United States of America

Abstract: Root causes of disease intuitively correspond to root vertices of a causal model that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms designed to automatically detect root causes from data. We seek a definition of patient-specific root causes of disease that models the intuitive procedure routinely utilized by physicians to uncover root causes in the clinic.We use structural equation models, interventional counterfactuals and the recently developed mathematical formalization of backtracking counterfactuals to propose a counterfactual formulation of patient-specific root causes of disease matching clinical intuition.We introduce a definition of patient-specific root causes of disease that climbs to the third rung of Pearl's Ladder of Causation and matches clinical intuition given factual patient data and a working causal model. We then show how to assign a root causal contribution score to each variable using Shapley values from explainable artificial intelligence.The proposed counterfactual formulation of patient-specific root causes of disease accounts for noisy labels, adapts to disease prevalence and admits fast computation without the need for counterfactual simulation.
Optical ReLU-like activation function based on a semiconductor laser with optical injection

Authors:
raw_author_name: Guan-Ting Liu
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech Universityhttps://ror.org/030bhh786, Shanghai 201210, China
raw_author_name: Yi-Wei Shen
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech Universityhttps://ror.org/030bhh786, Shanghai 201210, China
raw_author_name: Ruiqian Li
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech Universityhttps://ror.org/030bhh786, Shanghai 201210, China
raw_author_name: Jingyi Yu
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech Universityhttps://ror.org/030bhh786, Shanghai 201210, China
raw_author_name: Xuming He
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech Universityhttps://ror.org/030bhh786, Shanghai 201210, China
raw_author_name: Cheng Wang
raw_affiliation_string: Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, ShanghaiTech Universityhttps://ror.org/030bhh786, Shanghai 201210, China

Abstract: Artificial neural networks usually consist of successive linear multiply–accumulate operations and nonlinear activation functions. However, most optical neural networks only achieve the linear operation in the optical domain, while the optical implementation of activation function remains challenging. Here we present an optical ReLU-like activation function (with 180° rotation) based on a semiconductor laser subject to the optical injection in an experiment. The ReLU-like function is achieved in a broad regime above the Hopf bifurcation of the injection-locking diagram and is operated in the continuous-wave mode. In particular, the slope of the activation function is reconfigurable by tuning the frequency difference between the master laser and the slave laser.
A Dual Stream Fusion Network for Underwater Image Enhancement of Multi-scale Turbidity Restoration and Multi-path Color Correction

Authors:
raw_author_name: Kai Ji
raw_author_name: Weimin Lei
raw_author_name: Wei Zhang

Abstract: Underwater images frequently experience color distortion and blurred details due to the absorption and scattering of light, which can hinder underwater visual tasks. To address these challenges, we propose a dual-stream fusion network for enhancing underwater images. Our multi-scale turbidity restoration module (MTRM) adopts a two-stage dehazing process from coarse to fine, while employing the SOS boosting strategy and frequency-based dense connections to further improve the performance of the U-Net. The multi-path color correction module (MCCM) utilizes the multi-path residual block as the basic unit to construct RGB enhancement paths. It selectively establishes inter-color channels through attention-based cross connections, which efficiently harness the distinctive features from various color channels. Additionally, non-local spatial and channel attention provide essential correlation information for the final fusion stage. Qualitative and quantitative evaluations conducted on various underwater datasets have demonstrated the excellent performance of our method.
MTIE-Net: Multi-technology fusion of low-light image enhancement network

Authors:
raw_author_name: Hao Wu
raw_affiliation_string: Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, Sichuan Province, China; Automation and Information School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan Province, China
raw_author_name: Zhihao Ni
raw_affiliation_string: Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, Sichuan Province, China; Automation and Information School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan Province, China

Abstract: Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications.