pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysis
Authors: Meryem Abbad Andaloussi, Raphael Maser, Frank Hertel, François Lamoline, Andreas Dominik Husch
Date: 2024-08-27 00:00:00
Tasks: Medical Image Analysis, Tumor Segmentation

Publicly available data is essential for the progress of medical image analysis, in particular for crafting machine learning models. Glioma is the most common group of primary brain tumors, and magnetic resonance imaging (MRI) is a widely used modality in their diagnosis and treatment. However, the availability and quality of public datasets for glioma MRI are not well known. In this review, we searched for public datasets for glioma MRI using Google Dataset Search, The Cancer Imaging Archive (TCIA), and Synapse. A total of 28 datasets published between 2005 and May 2024 were found, containing 62019 images from 5515 patients. We analyzed the characteristics of these datasets, such as the origin, size, format, annotation, and accessibility. Additionally, we examined the distribution of tumor types, grades, and stages among the datasets. The implications of the evolution of the WHO classification on tumors of the brain are discussed, in particular the 2021 update that significantly changed the definition of glioblastoma. Additionally, potential research questions that could be explored using these datasets were highlighted, such as tumor evolution through malignant transformation, MRI normalization, and tumor segmentation. Interestingly, only two datasets among the 28 studied reflect the current WHO classification. This review provides a comprehensive overview of the publicly available datasets for glioma MRI currently at our disposal, providing aid to medical image analysis researchers in their decision-making on efficient dataset choice.
Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle
Authors: Yang Jiao, Hananeh Derakhshan, Barbara St. Pierre Schneider, Emma Regentova, Mei Yang
Date: 2024-08-26 00:00:00
Tasks: Edge Detection

White blood cells (WBCs) are the most diverse cell types observed in the healing process of injured skeletal muscles. In the course of healing, WBCs exhibit dynamic cellular response and undergo multiple protein expression changes. The progress of healing can be analyzed by quantifying the number of WBCs or the amount of specific proteins in light microscopic images obtained at different time points after injury. In this paper, we propose an automated quantifying and analysis framework to analyze WBCs using light microscopic images of uninjured and injured muscles. The proposed framework is based on the Localized Iterative Otsu's threshold method with muscle edge detection and region of interest extraction. Compared with the threshold methods used in ImageJ, the LI Otsu's threshold method has high resistance to background area and achieves better accuracy. The CD68-positive cell results are presented for demonstrating the effectiveness of the proposed work.
FCDM: A Physics-Guided Bidirectional Frequency Aware Convolution and Diffusion-Based Model for Sinogram Inpainting
Authors: Jiaze E, Srutarshi Banerjee, Tekin Bicer, Guannan Wang, yanfu Zhang, Bin Ren
Date: 2024-08-26 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Image Reconstruction, Scheduling, SSIM

Computed tomography (CT) is widely used in industrial and medical imaging, but sparse-view scanning reduces radiation exposure at the cost of incomplete sinograms and challenging reconstruction. Existing RGB-based inpainting models struggle with severe feature entanglement, while sinogram-specific methods often lack explicit physics constraints. We propose FCDM, a physics-guided, frequency-aware sinogram inpainting framework. It integrates bidirectional frequency-domain convolutions to disentangle overlapping features while enforcing total absorption and frequency-domain consistency via a physics-informed loss. To enhance diffusion-based restoration, we introduce a Fourier-enhanced mask embedding to encode angular dependencies and a frequency-adaptive noise scheduling strategy that incorporates a soft row-wise absorption constraint to maintain physical realism. Experiments on synthetic and real-world datasets show that FCDM outperforms existing methods, achieving SSIM over 0.95 and PSNR above 30 dB, with up to 33% and 29% improvements over baselines.
Exploiting XAI maps to improve MS lesion segmentation and detection in MRI
Authors: Federico Spagnolo, Nataliia Molchanova, Mario Ocampo Pineda, Lester Melie-Garcia, Meritxell Bach Cuadra, Cristina Granziera, Vincent Andrearczyk, Adrien Depeursinge
Date: 2024-08-21 00:00:00
Tasks: Lesion Segmentation, Segmentation, Semantic Segmentation

To date, several methods have been developed to explain deep learning algorithms for classification tasks. Recently, an adaptation of two of such methods has been proposed to generate instance-level explainable maps in a semantic segmentation scenario, such as multiple sclerosis (MS) lesion segmentation. In the mentioned work, a 3D U-Net was trained and tested for MS lesion segmentation, yielding an F1 score of 0.7006, and a positive predictive value (PPV) of 0.6265. The distribution of values in explainable maps exposed some differences between maps of true and false positive (TP/FP) examples. Inspired by those results, we explore in this paper the use of characteristics of lesion-specific saliency maps to refine segmentation and detection scores. We generate around 21000 maps from as many TP/FP lesions in a batch of 72 patients (training set) and 4868 from the 37 patients in the test set. 93 radiomic features extracted from the first set of maps were used to train a logistic regression model and classify TP versus FP. On the test set, F1 score and PPV were improved by a large margin when compared to the initial model, reaching 0.7450 and 0.7817, with 95% confidence intervals of [0.7358, 0.7547] and [0.7679, 0.7962], respectively. These results suggest that saliency maps can be used to refine prediction scores, boosting a model's performances.
AI, Entrepreneurs, and Privacy: Deep Learning Outperforms Humans in Detecting Entrepreneurs from Image Data
Authors: Martin Obschonka, Christian Fisch, Tharindu Fernando, Clinton Fookes
Date: 2024-08-19 00:00:00
Tasks: Contrastive Learning

Occupational outcomes like entrepreneurship are generally considered personal information that individuals should have the autonomy to disclose. With the advancing capability of artificial intelligence (AI) to infer private details from widely available human-centric data (e.g., social media), it is crucial to investigate whether AI can accurately extract private occupational information from such data. In this study, we demonstrate that deep neural networks can classify individuals as entrepreneurs with high accuracy based on facial images sourced from Crunchbase, a premier source for entrepreneurship data. Utilizing a dataset comprising facial images of 40,728 individuals, including both entrepreneurs and non-entrepreneurs, we train a Convolutional Neural Network (CNN) using a contrastive learning approach based on pairs of facial images (one entrepreneur and one non-entrepreneur per pair). While human experts (n=650) and trained participants (n=133) were unable to classify entrepreneurs with accuracy above chance levels (>50%), our AI model achieved a classification accuracy of 79.51%. Several robustness tests indicate that this high level of accuracy is maintained under various conditions. These results indicate privacy risks for entrepreneurs.
Modeling Human Strategy for Flattening Wrinkled Cloth Using Neural Networks
Authors: Nilay Kant, Ashrut Aryal, Rajiv Ranganathan, Ranjan Mukherjee, Charles Owen
Date: 2024-08-19 00:00:00

This paper explores a novel approach to model strategies for flattening wrinkled cloth learning from humans. A human participant study was conducted where the participants were presented with various wrinkle types and tasked with flattening the cloth using the fewest actions possible. A camera and Aruco marker were used to capture images of the cloth and finger movements, respectively. The human strategies for flattening the cloth were modeled using a supervised regression neural network, where the cloth images served as input and the human actions as output. Before training the neural network, a series of image processing techniques were applied, followed by Principal Component Analysis (PCA) to extract relevant features from each image and reduce the input dimensionality. This reduction decreased the model's complexity and computational cost. The actions predicted by the neural network closely matched the actual human actions on an independent data set, demonstrating the effectiveness of neural networks in modeling human actions for flattening wrinkled cloth.
Tracking Network Dynamics using Probabilistic State-Space Models
Authors: Victor M. Tenorio, Elvin Isufi, Geert Leus, Antonio G. Marques
Date: 2024-09-12 00:00:00
Tasks: State Space Models

This paper introduces a probabilistic approach for tracking the dynamics of unweighted and directed graphs using state-space models (SSMs). Unlike conventional topology inference methods that assume static graphs and generate point-wise estimates, our method accounts for dynamic changes in the network structure over time. We model the network at each timestep as the state of the SSM, and use observations to update beliefs that quantify the probability of the network being in a particular state. Then, by considering the dynamics of transition and observation models through the update and prediction steps, respectively, the proposed method can incorporate the information of real-time graph signals into the beliefs. These beliefs provide a probability distribution of the network at each timestep, being able to provide both an estimate for the network and the uncertainty it entails. Our approach is evaluated through experiments with synthetic and real-world networks. The results demonstrate that our method effectively estimates network states and accounts for the uncertainty in the data, outperforming traditional techniques such as recursive least squares.
Positioning and transmission in cell-free networks: ambiguity function, and MRC/MRT array gains
Authors: Luc Vandendorpe, Laurence Defraigne, Guillaume Thiran, Thomas Pairon, Christophe Craeye
Date: 2024-09-12 00:00:00
Tasks: ISAC

Cell-free network is a new paradigm, originating from distributed MIMO, that has been investigated for a few recent years as an alternative to the celebrated cellular structure. Future networks not only consider classical data transmission but also positioning, along the lines of Integrated Communications and Sensing (ISAC). The goal of this paper is to investigate at the same time the ambiguity function which is an important metric for positioning and the understanding of its associated resolution and ambiguities, and the array gain when maximum ratio transmission (MRT) or MR combining (MRC) is implemented for data communications. In particular, the role and impact of using a waveform with non-zero bandwidth is investigated. The theoretical findings are illustrated by means of computational results.
Millimeter-Wave Integrated Silicon Devices: Active versus Passive -- The Eternal Struggle Between Good and Evil
Authors: Michele Spasaro, Domenico Zito
Date: 2024-09-12 00:00:00

With the extreme scaling, active devices in both CMOS and BiCMOS technologies have reached outstanding ft/fmax, enabling an ever-increasing number of existing and emerging applications in the microwave and millimeter wave (mm-wave) frequency range. The increase in transistors ft/fmax has been so much significant that the performance of microwave and mm-wave ICs are limited mainly by losses in passive devices. In this paper, we address a discussion on qualitative and quantitative aspects that may help to further unveil the impact of such losses on the overall circuit performance and stimulate the adoption of effective loss-aware design methodologies. As example, we report the results related to the design of low power mm-wave low noise amplifiers (LNAs). Our results show how, in low power regime, the performances of mm-wave LNAs are dominated by losses in passive components. We also show how loss-aware design methodologies can mitigate the performance degradation due to passives, resulting as an important tool to get the full potential out of the active devices available today.
Differences between Two Maximal Principal Strain Rate Calculation Schemes in Traumatic Brain Analysis with in-vivo and in-silico Datasets
Authors: Xianghao Zhan, Zhou Zhou, Yuzhe Liu, Nicholas J. Cecchi, Marzieh Hajiahamemar, Michael M. Zeineh, Gerald A. Grant, David Camarillo
Date: 2024-09-12 00:00:00

Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPSxSR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one (MPSR1) is to use the time derivative of MPS, and another (MPSR2) is to use the first eigenvalue of the strain rate tensor. Both MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To quantify the discrepancies between these two methodologies, we conducted a comparison of these two MPSR methodologies across nine in-vivo and in-silico head impact datasets and found that 95MPSR1 was 5.87% larger than 95MPSR2, and 95MPSxSR1 was 2.55% larger than 95MPSxSR2. Across every element in all head impacts, MPSR1 was 8.28% smaller than MPSR2, and MPSxSR1 was 8.11% smaller than MPSxSR2. Furthermore, logistic regression models were trained to predict TBI based on the MPSR (or MPSxSR), and no significant difference was observed in the predictability across different variables. The consequence of misuse of MPSR and MPSxSR thresholds (i.e. compare threshold of 95MPSR1 with value from 95MPSR2 to determine if the impact is injurious) was investigated, and the resulting false rates were found to be around 1%. The evidence suggested that these two methodologies were not significantly different in detecting TBI.
Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications
Authors: Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin
Date: 2024-09-12 00:00:00

In the realm of reconfigurable intelligent surface (RIS)-assisted communication systems, the connection between a base station (BS) and user equipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE channels. Due to the fixed positioning of the BS and RIS and the mobility of UE, these two channels generally exhibit different time-varying characteristics, which are challenging to identify and exploit for feedback overhead reduction, given the separate channel estimation difficulty. To address this challenge, this letter introduces an innovative deep learning-based framework tailored for cascaded channel feedback, ingeniously capturing the intrinsic time variation in the cascaded channel. When an entire cascaded channel has been sent to the BS, this framework advocates the feedback of an efficient representation of this variation within a subsequent period through an extraction-compression scheme. This scheme involves RIS unit-grained channel variation extraction, followed by autoencoder-based deep compression to enhance compactness. Numerical simulations confirm that this feedback framework significantly reduces both the feedback and computational burdens.
Electromagnetic Normalization of Channel Matrix for Holographic MIMO Communications
Authors: Shuai S. A. Yuan, Li Wei, Xiaoming Chen, Chongwen Huang, Wei E. I. Sha
Date: 2024-09-12 00:00:00

Holographic multiple-input and multiple-output (MIMO) communications introduce innovative antenna array configurations, such as dense arrays and volumetric arrays, which offer notable advantages over conventional planar arrays with half-wavelength element spacing. However, accurately assessing the performance of these new holographic MIMO systems necessitates careful consideration of channel matrix normalization, as it is influenced by array gain, which, in turn, depends on the array topology. Traditional normalization methods may be insufficient for assessing these advanced array topologies, potentially resulting in misleading or inaccurate evaluations. In this study, we propose electromagnetic normalization approaches for the channel matrix that accommodate arbitrary array topologies, drawing on the array gains from analytical, physical, and full-wave methods. Additionally, we introduce a normalization method for near-field MIMO channels based on a rigorous dyadic Green's function approach, which accounts for potential losses of gain at near field. Finally, we perform capacity analyses under quasi-static, ergodic, and near-field conditions, through adopting the proposed normalization techniques. Our findings indicate that channel matrix normalization should reflect the realized gains of the antenna array in target directions. Failing to accurately normalize the channel matrix can result in errors when evaluating the performance limits and benefits of unconventional holographic array topologies, potentially compromising the optimal design of holographic MIMO systems.
Dequantization of a signal from two parallel quantized observations
Authors: Vojtěch Kovanda, Pavel Rajmic
Date: 2024-09-12 00:00:00
Tasks: Quantization

We propose a technique of signal acquisition using a combination of two devices with different sampling rates and quantization accuracies. Subsequent processing involving sparsity regularization enables us to reconstruct the signal at such a sampling frequency and with such a bit depth that was not possible using the two devices independently. Objective and subjective tests show the superiority of the proposed method in comparison with alternatives.
A White-Box Deep-Learning Method for Electrical Energy System Modeling Based on Kolmogorov-Arnold Network
Authors: Zhenghao Zhou, Yiyan Li, Zelin Guo, Zheng Yan, Mo-Yuen Chow
Date: 2024-09-12 00:00:00
Tasks: Deep Learning

Deep learning methods have been widely used as an end-to-end modeling strategy of electrical energy systems because of their conveniency and powerful pattern recognition capability. However, due to the "black-box" nature, deep learning methods have long been blamed for their poor interpretability when modeling a physical system. In this paper, we introduce a novel neural network structure, Kolmogorov-Arnold Network (KAN), to achieve "white-box" modeling for electrical energy systems to enhance the interpretability. The most distinct feature of KAN lies in the learnable activation function together with the sparse training and symbolification process. Consequently, KAN can express the physical process with concise and explicit mathematical formulas while remaining the nonlinear-fitting capability of deep neural networks. Simulation results based on three electrical energy systems demonstrate the effectiveness of KAN in the aspects of interpretability, accuracy, robustness and generalization ability.
Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications
Authors: Van-Phuc Bui, Pedro Maia de Sant Ana, Soheil Gherekhloo, Shashi Raj Pandey, Petar Popovski
Date: 2024-09-12 00:00:00
Tasks: ISAC, Reinforcement Learning (RL)

This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency.
Deep Learning for Personalized Electrocardiogram Diagnosis: A Review
Authors: Cheng Ding, Tianliang Yao, Chenwei Wu, Jianyuan Ni
Date: 2024-09-12 00:00:00
Tasks: Articles, Deep Learning

The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep learning has heralded a revolutionary era in medical data analysis, particularly in the domain of ECG diagnostics. However, inter-patient variability prohibit the generalibility of ECG-AI model trained on a population dataset, hence degrade the performance of ECG-AI on specific patient or patient group. Many studies have address this challenge using different deep learning technologies. This comprehensive review systematically synthesizes research from a wide range of studies to provide an in-depth examination of cutting-edge deep-learning techniques in personalized ECG diagnosis. The review outlines a rigorous methodology for the selection of pertinent scholarly articles and offers a comprehensive overview of deep learning approaches applied to personalized ECG diagnostics. Moreover, the challenges these methods encounter are investigated, along with future research directions, culminating in insights into how the integration of deep learning can transform personalized ECG diagnosis and enhance cardiac care. By emphasizing both the strengths and limitations of current methodologies, this review underscores the immense potential of deep learning to refine and redefine ECG analysis in clinical practice, paving the way for more accurate, efficient, and personalized cardiac diagnostics.
Smart CSI Processing for Accruate Commodity WiFi-based Humidity Sensing
Authors: Yirui Deng, Deepak Mishra, Shaghik Atakaramians, Aruna Seneviratne
Date: 2024-09-12 00:00:00
Tasks: Integrated sensing and communication, ISAC

Indoor humidity is a crucial factor affecting people's health and well-being. Wireless humidity sensing techniques are scalable and low-cost, making them a promising solution for measuring humidity in indoor environments without requiring additional devices. Such, machine learning (ML) assisted WiFi sensing is being envisioned as the key enabler for integrated sensing and communication (ISAC). However, the current WiFi-based sensing systems, such as WiHumidity, suffer from low accuracy. We propose an enhanced WiFi-based humidity detection framework to address this issue that utilizes innovative filtering and data processing techniques to exploit humidity-specific channel state information (CSI) signatures during RF sensing. These signals are then fed into ML algorithms for detecting different humidity levels. Specifically, our improved de-noising solution for the CSI captured by commodity hardware for WiFi sensing, combined with the k-th nearest neighbour ML algorithm and resolution tuning technique, helps improve humidity sensing accuracy. Our commercially available hardware-based experiments provide insights into achievable sensing resolution. Our empirical investigation shows that our enhanced framework can improve the accuracy of humidity sensing to 97%.