pwc-archive/papers-with-abstracts huggingface dataset:
Deconver: A Deconvolutional Network for Medical Image Segmentation
Authors: Pooya Ashtari, Shahryar Noei, Fateme Nateghi Haredasht, Jonathan H. Chen, Giuseppe Jurman, Aleksandra Pizurica, Sabine Van Huffel
Date: 2025-04-01 00:00:00
Tasks: Image Restoration, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in CNNs and high computational complexity in ViTs. This paper introduces Deconver, a novel network that integrates traditional deconvolution techniques from image restoration as a core learnable component within a U-shaped architecture. Deconver replaces computationally expensive attention mechanisms with efficient nonnegative deconvolution (NDC) operations, enabling the restoration of high-frequency details while suppressing artifacts. Key innovations include a backpropagation-friendly NDC layer based on a provably monotonic update rule and a parameter-efficient design. Evaluated across four datasets (ISLES'22, BraTS'23, GlaS, FIVES) covering both 2D and 3D segmentation tasks, Deconver achieves state-of-the-art performance in Dice scores and Hausdorff distance while reducing computational costs (FLOPs) by up to 90% compared to leading baselines. By bridging traditional image restoration with deep learning, this work offers a practical solution for high-precision segmentation in resource-constrained clinical workflows. The project is available at https://github.com/pashtari/deconver.
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
Authors: Basar Demir, Yikang Liu, Xiao Chen, Eric Z. Chen, Lin Zhao, Boris Mailhe, Terrence Chen, Shanhui Sun
Date: 2025-03-31 00:00:00
Tasks: Denoising, Image Denoising, Medical Image Denoising

Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are essential for medical applications. In this paper, we propose DiffDenoise, a powerful self-supervised denoising approach tailored for medical images, designed to preserve high-frequency details. Our approach comprises three stages. First, we train a diffusion model on noisy images, using the outputs of a pretrained Blind-Spot Network as conditioning inputs. Next, we introduce a novel stabilized reverse sampling technique, which generates clean images by averaging diffusion sampling outputs initialized with a pair of symmetric noises. Finally, we train a supervised denoising network using noisy images paired with the denoised outputs generated by the diffusion model. Our results demonstrate that DiffDenoise outperforms existing state-of-the-art methods in both synthetic and real-world medical image denoising tasks. We provide both a theoretical foundation and practical insights, demonstrating the method's effectiveness across various medical imaging modalities and anatomical structures.
Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models
Authors: Ahmed M. Taha, Salah A. Aly, Mohamed F. Darwish
Date: 2025-03-31 00:00:00
Tasks: Diagnostic, Transfer Learning

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuracies; and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics.
Exact local recovery for Chemical Shift Imaging
Authors: Cristobal Arrieta, Carlos A. Sing Long
Date: 2025-03-31 00:00:00

Chemical Shift Imaging (CSI) or Chemical Shift Encoded Magnetic Resonance Imaging (CSE-MRI) enables the quantification of different chemical species in the human body, and it is one of the most widely used imaging modalities used to quantify fat in the human body. Although there have been substantial improvements in the design of signal acquisition protocols and the development of a variety of methods for the recovery of parameters of interest from the measured signal, it is still challenging to obtain a consistent and reliable quantification over the entire field of view. In fact, there are still discrepancies in the quantities recovered by different methods, and each exhibits a different degree of sensitivity to acquisition parameters such as the choice of echo times. Some of these challenges have their origin in the signal model itself. In particular, it is non-linear, and there may be different sets of parameters of interest compatible with the measured signal. For this reason, a thorough analysis of this model may help mitigate some of the remaining challenges, and yield insight into novel acquisition protocols. In this work, we perform an analysis of the signal model underlying CSI, focusing on finding suitable conditions under which recovery of the parameters of interest is possible. We determine the sources of non-identifiability of the parameters, and we propose a reconstruction method based on smooth non-convex optimization under convex constraints that achieves exact local recovery under suitable conditions. A surprising result is that the concentrations of the chemical species in the sample may be identifiable even when other parameters are not. We present numerical results illustrating how our theoretical results may help develop novel acquisition techniques, and showing how our proposed recovery method yields results comparable to the state-of-the-art.
Novel sparse PCA method via Runge Kutta numerical method(s) for face recognition
Authors: Loc Hoang Tran, Luong Anh Tuan Nguyen
Date: 2025-03-30 00:00:00
Tasks: Face Recognition

Face recognition is a crucial topic in data science and biometric security, with applications spanning military, finance, and retail industries. This paper explores the implementation of sparse Principal Component Analysis (PCA) using the Proximal Gradient method (also known as ISTA) and the Runge-Kutta numerical methods. To address the face recognition problem, we integrate sparse PCA with either the k-nearest neighbor method or the kernel ridge regression method. Experimental results demonstrate that combining sparse PCA-solved via the Proximal Gradient method or the Runge-Kutta numerical approach-with a classification system yields higher accuracy compared to standard PCA. Additionally, we observe that the Runge-Kutta-based sparse PCA computation consistently outperforms the Proximal Gradient method in terms of speed.
Diffusion models applied to skin and oral cancer classification
Authors: José J. M. Uliana, Renato A. Krohling
Date: 2025-03-28 00:00:00
Tasks: Binary Classification, Cancer Classification, Classification, image-classification, Image Classification, Medical Image Classification, Oral Cancer Classification

This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifically, for the PAD-UFES-20 dataset, the model achieved a balanced accuracy of 0.6457 for six-class classification and 0.8357 for binary classification (cancer vs. non-cancer). For the P-NDB-UFES dataset, it attained a balanced accuracy of 0.9050. These results suggest that diffusion models are viable models for classifying medical images of skin and oral lesions. In addition, we investigate the robustness of the model trained on PAD-UFES-20 for skin cancer but tested on the clinical images of the HIBA dataset.
A Novel Distance-Based Metric for Quality Assessment in Image Segmentation
Authors: Niklas Rottmayer, Claudia Redenbach
Date: 2025-03-28 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The assessment of segmentation quality plays a fundamental role in the development, optimization, and comparison of segmentation methods which are used in a wide range of applications. With few exceptions, quality assessment is performed using traditional metrics, which are based on counting the number of erroneous pixels but do not capture the spatial distribution of errors. Established distance-based metrics such as the average Hausdorff distance are difficult to interpret and compare for different methods and datasets. In this paper, we introduce the Surface Consistency Coefficient (SCC), a novel distance-based quality metric that quantifies the spatial distribution of errors based on their proximity to the surface of the structure. Through a rigorous analysis using synthetic data and real segmentation results, we demonstrate the robustness and effectiveness of SCC in distinguishing errors near the surface from those further away. At the same time, SCC is easy to interpret and comparable across different structural contexts.
Laboratory evaluation of a wearable instrumented headband for rotational head kinematics measurement
Authors: Anu Tripathi, Yang Wan, Sushant Malave, Sheila Turcsanyi, Alice Lux Fawzi, Alison Brooks, Haneesh Kesari, Traci Snedden, Peter Ferrazzano, Christian Franck, Rika Carlsen
Date: 2025-04-02 00:00:00

Mild traumatic brain injuries (mTBI) are a highly prevalent condition with heterogeneous outcomes between individuals. A key factor governing brain tissue deformation and the risk of mTBI is the rotational kinematics of the head. Instrumented mouthguards are a widely accepted method for measuring rotational head motions, owing to their robust sensor-skull coupling. However, wearing mouthguards is not feasible in all situations, especially for long-term data collection. Therefore, alternative wearable devices are needed. In this study, we present an improved design and data processing scheme for an instrumented headband. Our instrumented headband utilizes an array of inertial measurement units (IMUs) and a new data-processing scheme based on continuous wavelet transforms to address sources of error in the IMU measurements. The headband performance was evaluated in the laboratory on an anthropomorphic test device, which was impacted with a soccer ball to replicate soccer heading. When comparing the measured peak rotational velocities (PRV) and peak rotational accelerations (PRA) between the reference sensors and the headband for impacts to the front of the head, the correlation coefficients (r) were 0.80 and 0.63, and the normalized root mean square error (NRMSE) values were 0.20 and 0.28, respectively. However, when considering all impact locations, r dropped to 0.42 and 0.34 and NRMSE increased to 0.5 and 0.41 for PRV and PRA, respectively. This new instrumented headband improves upon previous headband designs in reconstructing the rotational head kinematics resulting from frontal soccer ball impacts, providing a potential alternative to instrumented mouthguards.
Antenna Selection for Enhancing Privacy in Radar-Based Vital Sign Monitoring Systems
Authors: Zhihao Tao, Athina P. Petropulu
Date: 2025-04-02 00:00:00

Radar-based vital sign monitoring (VSM) systems have become valuable for non-contact health monitoring by detecting physiological activities, such as respiration and heartbeat, remotely. However, the conventional phased array used in VSM is vulnerable to privacy breaches, as an eavesdropper can extract sensitive vital sign information by analyzing the reflected radar signals. In this paper, we propose a novel approach to protect privacy in radar-based VSM by modifying the radar transmitter hardware, specifically by strategically selecting the transmit antennas from the available antennas in the transmit array. By dynamically selecting which antennas connect or disconnect to the radio frequency chain, the transmitter introduces additional phase noise to the radar echoes, generating false frequencies in the power spectrum of the extracted phases at the eavesdropper's receiver. The antenna activation pattern is designed to maximize the variance of the phases introduced by antenna selection, which effectively makes the false frequencies dominate the spectrum, obscuring the actual vital sign frequencies. Meanwhile, the authorized receiver, having knowledge of the antenna selection pattern, can compensate for the phase noise and accurately extract the vital signs. Numerical experiments are conducted to validate the effectiveness of the proposed approach in enhancing privacy while maintaining vital sign monitoring.
Identification of additive multivariable continuous-time systems
Authors: Maarten van der Hulst, Rodrigo González, Koen Classens, Nic Dirkx, Jeroen van de Wijdeven, Tom Oomen
Date: 2025-04-02 00:00:00
Tasks: Additive models

Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main objective of this paper is to develop a parametric identification strategy that delivers accurate and physically relevant models of multivariable systems using time-domain data. The introduced approach adopts an additive model structure, offering a parsimonious and interpretable representation of many physical systems, and employs a refined instrumental variable-based estimation algorithm. The developed identification method enables the estimation of parametric continuous-time additive models and is applicable to both open and closed-loop controlled systems. The performance of the estimator is demonstrated through numerical simulations and experimentally validated on a flexible beam system.
SpikeSift: A Computationally Efficient and Drift-Resilient Spike Sorting Algorithm
Authors: Vasileios Georgiadis, Panagiotis C. Petrantonakis
Date: 2025-04-02 00:00:00
Tasks: Clustering, Computational Efficiency, Spike Sorting

Objective: Spike sorting is a fundamental step in analysing extracellular recordings, enabling the isolation of single-neuron activity. However, it remains a challenging problem because extracellular traces mix overlapping spikes from neighbouring cells and are marred by recording instabilities such as electrode drift. Numerous algorithms have been proposed, yet many struggle to balance accuracy and computational efficiency, limiting their practicality for todays large-scale datasets. Approach: In response, we introduce SpikeSift, a spike-sorting algorithm expressly designed to mitigate drift while running on standard CPUs. SpikeSift (i) partitions long recordings into shorter, relatively stationary segments, (ii) carries out spike detection and clustering simultaneously through an iterative detect-and-subtract scheme within each segment, and (iii) preserves neuronal identity across segments via a fast template alignment stage that dispenses with continuous trajectory estimation. Main results: Extensive validation on paired intracellularly validated datasets and on biophysically realistic MEArec simulations covering elevated noise, diverse drift profiles, ultra short recordings and bursting activity, demonstrates that SpikeSift matches or exceeds the accuracy of state of the art methods while completing sorting an order of magnitude faster on a single desktop core. Significance: The combination of high fidelity, drift resilience, and modest computational demand renders SpikeSift broadly accessible while preserving data quality for downstream neurophysiological analysis
A Novel Pilot Allocation Technique for Uplink OFDMA in ISAC Systems
Authors: Ahmet Sacid Sumer, Ebubekir Memisoglu, Huseyin Arslan
Date: 2025-04-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

In integrated sensing and communication (ISAC) systems, pilot signals play a crucial role in enhancing sensing performance due to their strong autocorrelation properties and high transmission power. However, conventional interleaved pilots inherently constrain the maximum unambiguous range and reduce the accuracy of channel impulse response (CIR) estimation compared to continuous orthogonal frequency-division multiple access (OFDMA) signals. To address this challenge, we propose a novel overlapped block-pilot structure for uplink OFDMA-based ISAC systems, called phase-shifted ISAC (PS-ISAC) pilot allocation. The proposed method leverages a cyclic prefix (CP)-based phase-shifted pilot design, enabling efficient multi-transmitter pilot separation at the receiver. Simulation results confirm that the proposed scheme enhances CIR separation, reduces computational complexity, and improves mean square error (MSE) performance under practical power constraints. Furthermore, we demonstrate that utilizing continuous pilot resources maximizes the unambiguous range.
Coarse-to-Fine Semantic Communication Systems for Text Transmission
Authors: Mengli Tao, Jiancun Fan, Jie Luo, Huiqiang Xie
Date: 2025-04-02 00:00:00
Tasks: Semantic Communication, Sentence, Sentence Similarity

Achieving more powerful semantic representations and semantic understanding is one of the key problems in improving the performance of semantic communication systems. This work focuses on enhancing the semantic understanding of the text data to improve the effectiveness of semantic exchange. We propose a novel semantic communication system for text transmission, in which the semantic understanding is enhanced by coarse-to-fine processing. Especially, a dual attention mechanism is proposed to capture both the coarse and fine semantic information. Numerical experiments show the proposed system outperforms the benchmarks in terms of bilingual evaluation, sentence similarity, and robustness under various channel conditions.
Simultaneous Pre-compensation for Bandwidth Limitation and Fiber Dispersion in Cost-Sensitive IM/DD Transmission Systems
Authors: Zhe Zhao, Aiying Yang, Xiaoqian Huang, Peng Guo, Shuhua Zhao, Tianjia Xu, Wenkai Wan, Tianwai Bo, Zhongwei Tan, Yi Dong, Yaojun Qiao
Date: 2025-04-02 00:00:00

We propose a pre-compensation scheme for bandwidth limitation and fiber dispersion (pre-BL-EDC) based on the modified Gerchberg-Saxton (GS) algorithm. Experimental results demonstrate 1.0/1.0/2.0 dB gains compared to modified GS pre-EDC for 20/28/32 Gbit/s bandwidth-limited systems.
Reconfigurable Codebook-Based Beamforming for RDARS-Aided mmWave MU-MIMO Systems
Authors: Chengwang Ji, Qing Xue, Haiquan Lu, Jintao Wang, Qiaoyan Peng, Shaodan Ma, Wei zhang
Date: 2025-04-02 00:00:00

Reconfigurable distributed antenna and reflecting surface (RDARS) is a new architecture for the sixth-generation (6G) millimeter wave (mmWave) communications. In RDARS-aided mmWave systems, the active and passive beamforming design and working mode configuration for reconfigurable elements are crucial for system performance. In this paper, we aim to maximize the weighted sum rate (WSR) in the RDARS-aided mmWave system. To take advantage of RDARS, we first design a reconfigurable codebook (RCB) in which the number and dimension of the codeword can be flexibly adjusted. Then, a low overhead beam training scheme based on hierarchical search is proposed. Accordingly, the active and passive beamforming for data transmission is designed to achieve the maximum WSR for both space-division multiple access (SDMA) and time-division multiple access (TDMA) schemes. For the TDMA scheme, the optimal number of RDARS transmit elements and the allocated power budget for WSR maximization are derived in closed form. Besides, the superiority of the RDARS is verified and the conditions under which RDARS outperforms RIS and DAS are given. For the SDMA scheme, we characterize the relationship between the number of RDARS connected elements and the user distribution, followed by the derivation of the optimal placement positions of the RDARS transmit elements. High-quality beamforming design solutions are derived to minimize the inter-user interference (IUI) at the base station and RDARS side respectively, which nearly leads to the maximal WSR. Finally, simulation results confirm our theoretical findings and the superiority of the proposed schemes.
Low-Complexity Channel Estimation for RIS-Assisted ISAC System
Authors: Chen Zhen, Li Jianqing, Zhang Haijun, Zhang Wei
Date: 2025-04-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC), assisted by reconfigurable intelligent surface (RIS) has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network. However, a significant challenge in RIS-ISAC systems is the acquisition of channel state information (CSI), largely due to co-channel interference, which hinders meeting the required reliability standards. To address this issue, a minimax-concave penalty (MCP)-based CSI refinement scheme is proposed. This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix. Unlike previous methods, our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead, and the near-optimal solution is derived for our studied RIS-ISAC scheme. The effectiveness of the element-grouping strategy is validated through simulation experiments, demonstrating superior channel estimation results when compared to existing benchmarks.
Resource Allocation for RIS-Assisted CoMP-NOMA Networks using Reinforcement Learning
Authors: Muhammad Umer, Muhammad Ahmed Mohsin, Huma Ghafoor, Syed Ali Hassan
Date: 2025-04-01 00:00:00
Tasks: Deep Reinforcement Learning

This thesis delves into the forefront of wireless communication by exploring the synergistic integration of three transformative technologies: STAR-RIS, CoMP, and NOMA. Driven by the ever-increasing demand for higher data rates, improved spectral efficiency, and expanded coverage in the evolving landscape of 6G development, this research investigates the potential of these technologies to revolutionize future wireless networks. The thesis analyzes the performance gains achievable through strategic deployment of STAR-RIS, focusing on mitigating inter-cell interference, enhancing signal strength, and extending coverage to cell-edge users. Resource sharing strategies for STAR-RIS elements are explored, optimizing both transmission and reflection functionalities. Analytical frameworks are developed to quantify the benefits of STAR-RIS assisted CoMP-NOMA networks under realistic channel conditions, deriving key performance metrics such as ergodic rates and outage probabilities. Additionally, the research delves into energy-efficient design approaches for CoMP-NOMA networks incorporating RIS, proposing novel RIS configurations and optimization algorithms to achieve a balance between performance and energy consumption. Furthermore, the application of Deep Reinforcement Learning (DRL) techniques for intelligent and adaptive optimization in aerial RIS-assisted CoMP-NOMA networks is explored, aiming to maximize network sum rate while meeting user quality of service requirements. Through a comprehensive investigation of these technologies and their synergistic potential, this thesis contributes valuable insights into the future of wireless communication, paving the way for the development of more efficient, reliable, and sustainable networks capable of meeting the demands of our increasingly connected world.