pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study
Authors: Hassan Jahanandish, Shengtian Sang, Cynthia Xinran Li, Sulaiman Vesal, Indrani Bhattacharya, Jeong Hoon Lee, Richard Fan, Geoffrey A. Sonna, Mirabela Rusu
Date: 2025-01-31 00:00:00
Tasks: Sensitivity, Specificity

Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-based detection of clinically significant prostate cancer (CsPCa). However, MRI-detected lesions must still be mapped to transrectal ultrasound (TRUS) images during biopsy, which results in missing CsPCa. This study systematically evaluates a multimodal AI framework integrating MRI and TRUS image sequences to enhance CsPCa identification. The study included 3110 patients from three cohorts across two institutions who underwent prostate biopsy. The proposed framework, based on the 3D UNet architecture, was evaluated on 1700 test cases, comparing performance to unimodal AI models that use either MRI or TRUS alone. Additionally, the proposed model was compared to radiologists in a cohort of 110 patients. The multimodal AI approach achieved superior sensitivity (80%) and Lesion Dice (42%) compared to unimodal MRI (73%, 30%) and TRUS models (49%, 27%). Compared to radiologists, the multimodal model showed higher specificity (88% vs. 78%) and Lesion Dice (38% vs. 33%), with equivalent sensitivity (79%). Our findings demonstrate the potential of multimodal AI to improve CsPCa lesion targeting during biopsy and treatment planning, surpassing current unimodal models and radiologists; ultimately improving outcomes for prostate cancer patients.
CerraData-4MM: A multimodal benchmark dataset on Cerrado for land use and land cover classification
Authors: Mateus de Souza Miranda, Ronny Hänsch, Valdivino Alexandre de Santiago Júnior, Thales Sehn Körting, Erison Carlos dos Santos Monteiro
Date: 2025-01-31 00:00:00
Tasks: Land Cover Classification, Semantic Segmentation

The Cerrado faces increasing environmental pressures, necessitating accurate land use and land cover (LULC) mapping despite challenges such as class imbalance and visually similar categories. To address this, we present CerraData-4MM, a multimodal dataset combining Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Imagery (MSI) with 10m spatial resolution. The dataset includes two hierarchical classification levels with 7 and 14 classes, respectively, focusing on the diverse Bico do Papagaio ecoregion. We highlight CerraData-4MM's capacity to benchmark advanced semantic segmentation techniques by evaluating a standard U-Net and a more sophisticated Vision Transformer (ViT) model. The ViT achieves superior performance in multimodal scenarios, with the highest macro F1-score of 57.60% and a mean Intersection over Union (mIoU) of 49.05% at the first hierarchical level. Both models struggle with minority classes, particularly at the second hierarchical level, where U-Net's performance drops to an F1-score of 18.16%. Class balancing improves representation for underrepresented classes but reduces overall accuracy, underscoring the trade-off in weighted training. CerraData-4MM offers a challenging benchmark for advancing deep learning models to handle class imbalance and multimodal data fusion. Code, trained models, and data are publicly available at https://github.com/ai4luc/CerraData-4MM.
Advanced Assessment of Stroke in Retinal Fundus Imaging with Deep Multi-view Learning
Authors: Aysen Degerli, Mika Hilvo, Juha Pajula, Petri Huhtinen, Pekka Jäkälä
Date: 2025-01-31 00:00:00
Tasks: MULTI-VIEW LEARNING

Stroke is globally a major cause of mortality and morbidity, and hence accurate and rapid diagnosis of stroke is valuable. Retinal fundus imaging reveals the known markers of elevated stroke risk in the eyes, which are retinal venular widening, arteriolar narrowing, and increased tortuosity. In contrast to other imaging techniques used for stroke diagnosis, the acquisition of fundus images is easy, non-invasive, fast, and inexpensive. Therefore, in this study, we propose a multi-view stroke network (MVS-Net) to detect stroke and transient ischemic attack (TIA) using retinal fundus images. Contrary to existing studies, our study proposes for the first time a solution to discriminate stroke and TIA with deep multi-view learning by proposing an end-to-end deep network, consisting of multi-view inputs of fundus images captured from both right and left eyes. Accordingly, the proposed MVS-Net defines representative features from fundus images of both eyes and determines the relation within their macula-centered and optic nerve head-centered views. Experiments performed on a dataset collected from stroke and TIA patients, in addition to healthy controls, show that the proposed framework achieves an AUC score of 0.84 for stroke and TIA detection.
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer
Authors: Surochita Pal, Sushmita Mitra
Date: 2025-01-31 00:00:00
Tasks: Computed Tomography (CT), Image Augmentation, Medical Image Analysis

This study focuses on the classification of cancerous and healthy slices from multimodal lung images. The data used in the research comprises Computed Tomography (CT) and Positron Emission Tomography (PET) images. The proposed strategy achieves the fusion of PET and CT images by utilizing Principal Component Analysis (PCA) and an Autoencoder. Subsequently, a new ensemble-based classifier developed, Deep Ensembled Multimodal Fusion (DEMF), employing majority voting to classify the sample images under examination. Gradient-weighted Class Activation Mapping (Grad-CAM) employed to visualize the classification accuracy of cancer-affected images. Given the limited sample size, a random image augmentation strategy employed during the training phase. The DEMF network helps mitigate the challenges of scarce data in computer-aided medical image analysis. The proposed network compared with state-of-the-art networks across three publicly available datasets. The network outperforms others based on the metrics - Accuracy, F1-Score, Precision, and Recall. The investigation results highlight the effectiveness of the proposed network.
LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical Images
Authors: Yujie Ding, Shenghua Teng, Zuoyong Li, Xiao Chen
Date: 2025-01-27 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.
Hybrid Fingerprint-based Positioning in Cell-Free Massive MIMO Systems
Authors: Manish Kumar, Tzu-Hsuan Chou, Byunghyun Lee, Nicolo Michelusi, David J. Love, James V. Krogmeier
Date: 2025-02-04 00:00:00
Tasks: GPR, regression

Recently, there has been an increasing interest in 6G technology for integrated sensing and communications, where positioning stands out as a key application. In the realm of 6G, cell-free massive multiple-input multiple-output (MIMO) systems, featuring distributed base stations equipped with a large number of antennas, present an abundant source of angle-of-arrival (AOA) information that could be exploited for positioning applications. In this paper we leverage this AOA information at the base stations using the multiple signal classification (MUSIC) algorithm, in conjunction with received signal strength (RSS) for positioning through Gaussian process regression (GPR). An AOA fingerprint database is constructed by capturing the angle data from multiple locations across the network area and is combined with RSS data from the same locations to form a hybrid fingerprint which is then used to train a GPR model employing a squared exponential kernel. The trained regression model is subsequently utilized to estimate the location of a user equipment. Simulations demonstrate that the GPR model with hybrid input achieves better positioning accuracy than traditional GPR models utilizing RSS-only and AOA-only inputs.
Simultaneous Beamforming and Anti-Jamming With Intelligent Omni-Surfaces
Authors: YuHan Wang, Shuhao Zeng, Qingyu Liu, Boya Di, Hongliang Zhang
Date: 2025-02-04 00:00:00

Wireless transmission is vulnerable to malicious jamming attacks due to the openness of wireless channels, posing a severe threat to wireless communications. Current anti-jamming studies primarily focus on either enhancing desired signals or mitigating jamming, resulting in limited performance. To address this issue, intelligent omni-surface (IOS) is a promising solution. By jointly designing its reflective and refractive properties, the IOS can simultaneously nullify jamming and enhance desired signals. In this paper, we consider an IOS-aided multi-user anti-jamming communication system, aiming to improve desired signals and nullify jamming by optimizing IOS phase shifts and transmit beamforming. However, this is challenging due to the coupled and discrete IOS reflection and refraction phase shifts, the unknown jammer's beamformer, and imperfect jammer-related channel state information. To tackle this, we relax IOS phase shifts to continuous states and optimize with a coupling-aware algorithm using the Cauchy-Schwarz inequality and S-procedure, followed by a local search to recover discrete states. Simulation results show that the proposed scheme significantly improves the sum rate amid jamming attacks.
ISAC MIMO Systems with OTFS Waveforms and Virtual Arrays
Authors: Kailong Wang, Athina Petropulu
Date: 2025-02-04 00:00:00
Tasks: ISAC

A novel Integrated Sensing-Communication (ISAC) system is proposed that can accommodate high mobility scenarios while making efficient use of bandwidth for both communication and sensing. The system comprises a monostatic multiple-input multiple-output (MIMO) radar that transmits orthogonal time frequency space (OTFS) waveforms. Bandwidth efficiency is achieved by making Doppler-delay (DD) domain bins available for shared use by the transmit antennas. For maximum communication rate, all DD-domain bins are used as shared, but in this case, the target resolution is limited by the aperture of the receive array. A low-complexity method is proposed for obtaining coarse estimates of the radar targets parameters in that case. A novel approach is also proposed to construct a virtual array (VA) for achieving a target resolution higher than that allowed by the receive array. The VA is formed by enforcing zeros on certain time-frequency (TF) domain bins, thereby creating private bins assigned to specific transmit antennas. The TF signals received on these private bins are orthogonal, enabling the synthesis of a VA. When combined with coarse target estimates, this approach provides high-accuracy target estimation. To preserve DD-domain information, the introduction of private bins requires reducing the number of DD-domain symbols, resulting in a trade-off between communication rate and sensing performance. However, even a small number of private bins is sufficient to achieve significant sensing gains with minimal communication rate loss. The proposed system is robust to Doppler frequency shifts that arise in high mobility scenarios.
Diffusion Model for Multiple Antenna Communications
Authors: Jia Guo, Xiaoxia Xu, Yuanwei Liu, Arumugam Nallanathan
Date: 2025-02-03 00:00:00
Tasks: Decision Making, model

The potential of applying diffusion models (DMs) for multiple antenna communications is discussed. A unified framework of applying DM for multiple antenna tasks is first proposed. Then, the tasks are innovatively divided into two categories, i.e., decision-making tasks and generation tasks, depending on whether an optimization of system parameters is involved. For each category, it is conceived 1) how the framework can be used for each task and 2) why the DM is superior to traditional artificial intelligence (TAI) and conventional optimization tasks. It is highlighted that the DMs are well-suited for scenarios with strong interference and noise, excelling in modeling complex data distribution and exploring better actions. A case study of learning beamforming with a DM is then provided, to demonstrate the superiority of the DMs with simulation results. Finally, the applications of DM for emerging multiple antenna technologies and promising research directions are discussed.
Impact of Altitude, Bandwidth, and NLOS Bias on TDOA-Based 3D UAV Localization: Experimental Results and CRLB Analysis
Authors: Cole Dickerson, Saad Masrur, Jonah Dickerson, Özgür Özdemir, Ismail Güvenç
Date: 2025-02-03 00:00:00

This paper investigates unmanned aerial vehicle (UAV) localization using time difference of arrival (TDOA) measurements under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. A 3D TDOA Cram\'er-Rao lower bound (CRLB) model is developed accounting for varying altitudes and signal bandwidths. The model is compared to five real-world UAV flight experiments conducted at different altitudes (40 m, 70 m, 100 m) and bandwidths (1.25 MHz, 2.5 MHz, 5 MHz) using Keysight N6841A radio frequency (RF) sensors of the NSF AERPAW platform. Results show that altitude, bandwidth, and NLOS obstructions significantly impact localization accuracy. Higher bandwidths enhance signal time resolution, while increased altitudes mitigate multipath and NLOS biases, both contributing to improved performance. However, hovering close to RF sensors degrades accuracy due to antenna pattern misalignment and geometric dilution of precision. These findings emphasize the inadequacy of traditional LOS-based models in NLOS environments and highlight the importance of adaptive approaches for accurate localization in challenging scenarios.
GPASS: Deep Learning for Beamforming in Pinching-Antenna Systems (PASS)
Authors: Jia Guo, Yuanwei Liu, Arumugam Nallanathan
Date: 2025-02-03 00:00:00
Tasks: Deep Learning

A novel GPASS architecture is proposed for jointly learning pinching beamforming and transmit beamforming in pinching antenna systems (PASS). The GPASS is with a staged architecture, where the positions of pinching antennas are first learned by a sub-GNN. Then, the transmit beamforming is learned by another sub-GNN based on the antenna positions. The sub-GNNs are incorporated with the permutation property of the beamforming policy, which helps improve the learning performance. The optimal solution structure of transmit beamforming is also leveraged to simplify the mappings to be learned. Numerical results demonstrate that the proposed architecture can achieve a higher SE than a heuristic baseline method with low inference complexity.
DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users
Authors: Nancy Nayak, Kin K. Leung, Lajos Hanzo
Date: 2025-02-03 00:00:00
Tasks: Computational Efficiency, Deep Reinforcement Learning

With the emergence of AI technologies in next-generation communication systems, machine learning plays a pivotal role due to its ability to address high-dimensional, non-stationary optimization problems within dynamic environments while maintaining computational efficiency. One such application is directional beamforming, achieved through learning-based blind beamforming techniques that utilize already existing radio frequency (RF) fingerprints of the user equipment obtained from the base stations and eliminate the need for additional hardware or channel and angle estimations. However, as the number of users and antenna dimensions increase, thereby expanding the problem's complexity, the learning process becomes increasingly challenging, and the performance of the learning-based method cannot match that of the optimal solution. In such a scenario, we propose a deep reinforcement learning-based blind beamforming technique using a learnable Dolph-Tschebyscheff antenna array that can change its beam pattern to accommodate mobile users. Our simulation results show that the proposed method can support data rates very close to the best possible values.
SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy
Authors: Miguel Bhagubai, Christos Chatzichristos, Lauren Swinnen, Jaiver Macea, Jingwei Zhang, Lieven Lagae, Katrien Jansen, Andreas Schulze-Bonhage, Francisco Sales, Benno Mahler, Yvonne Weber, Wim Van Paesschen, Maarten De Vos
Date: 2025-02-03 00:00:00
Tasks: Seizure Detection

The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments. The large amounts of data that can be recorded with such devices holds significant potential for developing automated seizure detection frameworks. In this work, we present SeizeIT2, the first open dataset of wearable data recorded in patients with focal epilepsy. The dataset comprises more than 11,000 hours of multimodal data, including behind-the-ear electroencephalography, electrocardiography, electromyography and movement (accelerometer and gyroscope) data. The dataset contains 886 focal seizures recorded from 125 patients across five different European Epileptic Monitoring Centers. We present a suggestive training/validation split to propel the development of AI methodologies for seizure detection, as well as two benchmark approaches and evaluation metrics. The dataset can be accessed on OpenNeuro and is stored in Brain Imaging Data Structure (BIDS) format.
General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor Types
Authors: M. Muzeau, J. Frontera-Pons, Chengfang Ren, J. -P. Ovarlez
Date: 2025-02-03 00:00:00
Tasks: Contrastive Learning

The increased availability of SAR data has raised a growing interest in applying deep learning algorithms. However, the limited availability of labeled data poses a significant challenge for supervised training. This article introduces a new method for classifying SAR data with minimal labeled images. The method is based on a feature extractor Vit trained with contrastive learning. It is trained on a dataset completely different from the one on which classification is made. The effectiveness of the method is assessed through 2D visualization using t-SNE for qualitative evaluation and k-NN classification with a small number of labeled data for quantitative evaluation. Notably, our results outperform a k-NN on data processed with PCA and a ResNet-34 specifically trained for the task, achieving a 95.9% accuracy on the MSTAR dataset with just ten labeled images per class.
Multi-target Range, Doppler and Angle estimation in MIMO-FMCW Radar with Limited Measurements
Authors: Chandrashekhar Rai, Himali Singh, Arpan Chattopadhyay
Date: 2025-02-03 00:00:00
Tasks: Compressive Sensing

Multiple-input multiple-output (MIMO) radar offers several performance and flexibility advantages over traditional radar arrays. However, high angular and Doppler resolutions necessitate a large number of antenna elements and the transmission of numerous chirps, leading to increased hardware and computational complexity. While compressive sensing (CS) has recently been applied to pulsed-waveform radars with sparse measurements, its application to frequency-modulated continuous wave (FMCW) radar for target detection remains largely unexplored. In this paper, we propose a novel CS-based multi-target localization algorithm in the range, Doppler, and angular domains for MIMO-FMCW radar, where we jointly estimate targets' velocities and angles of arrival. To this end, we present a signal model for sparse-random and uniform linear arrays based on three-dimensional spectral estimation. For range estimation, we propose a discrete Fourier transform (DFT)-based focusing and orthogonal matching pursuit (OMP)-based techniques, each with distinct advantages, while two-dimensional CS is used for joint Doppler-angle estimation. Leveraging the properties of structured random matrices, we establish theoretical uniform and non-uniform recovery guarantees with high probability for the proposed framework. Our numerical experiments demonstrate that our methods achieve similar detection performance and higher resolution compared to conventional DFT and MUSIC with fewer transmitted chirps and antenna elements.
Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings
Authors: Mithun Saha, Maxwell A. Xu, Wanting Mao, Sameer Neupane, James M. Rehg, Santosh Kumar
Date: 2025-02-03 00:00:00
Tasks: Photoplethysmography (PPG)

Photoplethysmography (PPG)-based foundation models are gaining traction due to the widespread use of PPG in biosignal monitoring and their potential to generalize across diverse health applications. In this paper, we introduce Pulse-PPG, the first open-source PPG foundation model trained exclusively on raw PPG data collected over a 100-day field study with 120 participants. Existing PPG foundation models are either open-source but trained on clinical data or closed-source, limiting their applicability in real-world settings. We evaluate Pulse-PPG across multiple datasets and downstream tasks, comparing its performance against a state-of-the-art foundation model trained on clinical data. Our results demonstrate that Pulse-PPG, trained on uncurated field data, exhibits superior generalization across clinical and mobile health applications in both lab and field settings. This suggests that exposure to real-world variability enables the model to learn fine-grained representations, making it more adaptable across tasks. Furthermore, pre-training on field data surprisingly outperforms its pre-training on clinical data in many tasks, reinforcing the importance of training on real-world, diverse datasets. To encourage further advancements in robust foundation models leveraging field data, we plan to release Pulse-PPG, providing researchers with a powerful resource for developing more generalizable PPG-based models.