pwc-archive/papers-with-abstracts huggingface dataset:
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms
Authors: Mohammad Reza Yousefi, Ali Bakrani, Amin Dehghani
Date: 2025-04-04 00:00:00
Tasks: Diagnostic, Dimensionality Reduction, Sensitivity, Transfer Learning

Diabetic Retinopathy (DR) is a serious and common complication of diabetes, caused by prolonged high blood sugar levels that damage the small retinal blood vessels. If left untreated, DR can progress to retinal vein occlusion and stimulate abnormal blood vessel growth, significantly increasing the risk of blindness. Traditional diabetes diagnosis methods often utilize convolutional neural networks (CNNs) to extract visual features from retinal images, followed by classification algorithms such as decision trees and k-nearest neighbors (KNN) for disease detection. However, these approaches face several challenges, including low accuracy and sensitivity, lengthy machine learning (ML) model training due to high data complexity and volume, and the use of limited datasets for testing and evaluation. This study investigates the application of transfer learning (TL) to enhance ML model performance in DR detection. Key improvements include dimensionality reduction, optimized learning rate adjustments, and advanced parameter tuning algorithms, aimed at increasing efficiency and diagnostic accuracy. The proposed model achieved an overall accuracy of 84% on the testing dataset, outperforming prior studies. The highest class-specific accuracy reached 89%, with a maximum sensitivity of 97% and an F1-score of 92%, demonstrating strong performance in identifying DR cases. These findings suggest that TL-based DR screening is a promising approach for early diagnosis, enabling timely interventions to prevent vision loss and improve patient outcomes.
Point Cloud Objective Quality: Benchmarking Features and Quality Evaluation
Authors: Joao Prazeres, Rafael Rodrigues, Manuela Pereira, Antonio M. G. Pinheiro
Date: 2025-04-04 00:00:00
Tasks: Attribute, Benchmarking, feature selection, Graph Similarity, regression

Full-reference point cloud objective metrics are currently providing very accurate representations of perceptual quality. These metrics are usually composed of a set of features that are somehow combined, resulting in a final quality value. In this study, the different features of the best-performing metrics are analyzed. For that, different objective quality metrics are compared between them, and the differences in their quality representation are studied. This provided a selection of the set of metrics used in this study, namely the point-to-plane, point-to-attribute, Point Cloud Structural Similarity, Point Cloud Quality Metric and Multiscale Graph Similarity. The features defined in those metrics are examined based on their contribution to the objective estimation using recursive feature elimination. To employ the recursive feature selection algorithm, both the support vector regression and the ridge regression algorithms were employed. For this study, the Broad Quality Assessment of Static Point Clouds in Compression Scenario database was used for both training and validation of the models. According to the recursive feature elimination, several features were selected and then combined using the regression method used to select those features. The best combination models were then evaluated across five different publicly available subjective quality assessment datasets, targeting different point cloud characteristics and distortions. It was concluded that a combination of features selected from the Point Cloud Quality Metric, Multiscale Graph Similarity and PSNR MSE D2, combined with Ridge Regression, results in the best performance. This model leads to the definition of the Feature Selection Model.
Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images
Authors: Fatemeh Javadian, Zahra Aminparast, Johannes Stegmaier, Abin Jose
Date: 2025-04-04 00:00:00
Tasks: Classification, Contrastive Learning, Diagnostic, Neural Architecture Search, Nuclei Classification

This study explores the application of supervised and unsupervised autoencoders (AEs) to automate nuclei classification in clear cell renal cell carcinoma (ccRCC) images, a diagnostic task traditionally reliant on subjective visual grading by pathologists. We evaluate various AE architectures, including standard AEs, contractive AEs (CAEs), and discriminative AEs (DAEs), as well as a classifier-based discriminative AE (CDAE), optimized using the hyperparameter tuning tool Optuna. Bhattacharyya distance is selected from several metrics to assess class separability in the latent space, revealing challenges in distinguishing adjacent grades using unsupervised models. CDAE, integrating a supervised classifier branch, demonstrated superior performance in both latent space separation and classification accuracy. Given that CDAE-CNN achieved notable improvements in classification metrics, affirming the value of supervised learning for class-specific feature extraction, F1 score was incorporated into the tuning process to optimize classification performance. Results show significant improvements in identifying aggressive ccRCC grades by leveraging the classification capability of AE through latent clustering followed by fine-grained classification. Our model outperforms the current state of the art, CHR-Network, across all evaluated metrics. These findings suggest that integrating a classifier branch in AEs, combined with neural architecture search and contrastive learning, enhances grading automation in ccRCC pathology, particularly in detecting aggressive tumor grades, and may improve diagnostic accuracy.
Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms
Authors: Junchi Zhou, Haozhou Wang, Yoichiro Kato, Tejasri Nampally, P. Rajalakshmi, M. Balram, Keisuke Katsura, Hao Lu, Yue Mu, Wanneng Yang, Yangmingrui Gao, Feng Xiao, Hongtao Chen, Yuhao Chen, Wenjuan Li, Jingwen Wang, Fenghua Yu, Jian Zhou, Wensheng Wang, Xiaochun Hu, Yuanzhu Yang, Yanfeng Ding, Wei Guo, Shouyang Liu
Date: 2025-04-02 00:00:00
Tasks: Benchmarking, Semantic Segmentation

Developing computer vision-based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops.
Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images
Authors: Ariadna Tohà-Dalmau, Josep Rosinés-Fonoll, Enrique Romero, Ferran Mazzanti, Ruben Martin-Pinardel, Sonia Marias-Perez, Carolina Bernal-Morales, Rafael Castro-Dominguez, Andrea Mendez, Emilio Ortega, Irene Vinagre, Marga Gimenez, Alfredo Vellido, Javier Zarranz-Ventura
Date: 2025-04-01 00:00:00

This study aimed to develop a machine learning (ML) algorithm capable of determining cardiovascular risk in multimodal retinal images from patients with type 1 diabetes mellitus, distinguishing between moderate, high, and very high-risk levels. Radiomic features were extracted from fundus retinography, optical coherence tomography (OCT), and OCT angiography (OCTA) images. ML models were trained using these features either individually or combined with clinical data. A dataset of 597 eyes (359 individuals) was analyzed, and models trained only with radiomic features achieved AUC values of (0.79 $\pm$ 0.03) for identifying moderate risk cases from high and very high-risk cases, and (0.73 $\pm$ 0.07) for distinguishing between high and very high-risk cases. The addition of clinical variables improved all AUC values, reaching (0.99 $\pm$ 0.01) for identifying moderate risk cases and (0.95 $\pm$ 0.02) for differentiating between high and very high-risk cases. For very high CV risk, radiomics combined with OCT+OCTA metrics and ocular data achieved an AUC of (0.89 $\pm$ 0.02) without systemic data input. These results demonstrate that radiomic features obtained from multimodal retinal images are useful for discriminating and classifying CV risk labels, highlighting the potential of this oculomics approach for CV risk assessment.
Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction
Authors: Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu
Date: 2025-04-04 00:00:00
Tasks: Data Augmentation

Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss predictions can optimize use of the radio frequency spectrum and minimize unwanted interference. Modern path loss modeling often leverages data-driven approaches, using machine learning to train models on drive test measurement datasets. Drive tests primarily represent downlink scenarios, where the Tx is located on a building and the Rx is located on a moving vehicle. Consequently, trained models are frequently reserved for downlink coverage estimation, lacking representation of uplink scenarios. In this paper, we demonstrate that data augmentation can be used to train a path loss model that is generalized to uplink, downlink, and backhaul scenarios, training using only downlink drive test measurements. By adding a small number of synthetic samples representing uplink scenarios to the training set, root mean squared error is reduced by >8 dB on uplink examples in the test set.
A New Statistical Approach to Calibration-Free Localization Using Unlabeled Crowdsourced Data
Authors: Haozhou Hu, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2025-04-04 00:00:00
Tasks: Indoor Localization

Fingerprinting-based indoor localization methods typically require labor-intensive site surveys to collect signal measurements at known reference locations and frequent recalibration, which limits their scalability. This paper addresses these challenges by presenting a novel approach for indoor localization that utilizes crowdsourced data without location labels. We leverage the statistical information of crowdsourced data and propose a cumulative distribution function (CDF) based distance estimation method that maps received signal strength (RSS) to distances from access points. This approach overcomes the limitations of conventional distance estimation based on the empirical path loss model by efficiently capturing the impacts of shadow fading and multipath. Compared to fingerprinting, our unsupervised statistical approach eliminates the need for signal measurements at known reference locations. The estimated distances are then integrated into a three-step framework to determine the target location. The localization performance of our proposed method is evaluated using RSS data generated from ray-tracing simulations. Our results demonstrate significant improvements in localization accuracy compared to methods based on the empirical path loss model. Furthermore, our statistical approach, which relies on unlabeled data, achieves localization accuracy comparable to that of the supervised approach, the $k$-Nearest Neighbor ($k$NN) algorithm, which requires fingerprints with location labels. For reproducibility and future research, we make the ray-tracing dataset publicly available at [2].
Scalable Hypergraph Structure Learning with Diverse Smoothness Priors
Authors: Benjamin T. Brown, Haoxiang Zhang, Daniel L. Lau, Gonzalo R. Arce
Date: 2025-04-04 00:00:00
Tasks: Graph Learning

In graph signal processing, learning the weighted connections between nodes from a set of sample signals is a fundamental task when the underlying relationships are not known a priori. This task is typically addressed by finding a graph Laplacian on which the observed signals are smooth. With the extension of graphs to hypergraphs - where edges can connect more than two nodes - graph learning methods have similarly been generalized to hypergraphs. However, the absence of a unified framework for calculating total variation has led to divergent definitions of smoothness and, consequently, differing approaches to hyperedge recovery. We confront this challenge through generalization of several previously proposed hypergraph total variations, subsequently allowing ease of substitution into a vector based optimization. To this end, we propose a novel hypergraph learning method that recovers a hypergraph topology from time-series signals based on a smoothness prior. Our approach addresses key limitations in prior works, such as hyperedge selection and convergence issues, by formulating the problem as a convex optimization solved via a forward-backward-forward algorithm, ensuring guaranteed convergence. Additionally, we introduce a process that simultaneously limits the span of the hyperedge search and maintains a valid hyperedge selection set. In doing so, our method becomes scalable in increasingly complex network structures. The experimental results demonstrate improved performance, in terms of accuracy, over other state-of-the-art hypergraph inference methods; furthermore, we empirically show our method to be robust to total variation terms, biased towards global smoothness, and scalable to larger hypergraphs.
Fair and Energy-Efficient Activation Control Mechanisms for Repeater-Assisted Massive MIMO
Authors: Ozan Alp Topal, Özlem Tuğfe Demir, Emil Björnson, Cicek Cavdar
Date: 2025-04-04 00:00:00
Tasks: Fairness

Massive multiple-input multiple-output (mMIMO) has been the core of 5G due to its ability to improve spectral efficiency and spatial multiplexing significantly; however, cell-edge users still experience performance degradation due to inter-cell interference and uneven signal distribution. While cell-free mMIMO (cfmMIMO) addresses this issue by providing uniform coverage through distributed antennas, it requires significantly more deployment cost due to the fronthaul and tight synchronization requirements. Alternatively, repeater-assisted massive MIMO (RA-MIMO) has recently been proposed to extend the coverage of cellular mMIMO by densely deploying low-cost single-antenna repeaters capable of amplifying and forwarding signals. In this work, we investigate amplification control for the repeaters for two different goals: (i) providing a fair performance among users, and (ii) reducing the extra energy consumption by the deployed repeaters. We propose a max-min amplification control algorithm using the convex-concave procedure for fairness and a joint sleep mode and amplification control algorithm for energy efficiency, comparing long- and short-term strategies. Numerical results show that RA-MIMO, with maximum amplification, improves signal-to-interference-plus-noise ratio (SINR) by over 20 dB compared to mMIMO and performs within 1 dB of cfmMIMO when deploying the same number of repeaters as access points in cfmMIMO. Additionally, our majority-rule-based long-term sleep mechanism reduces repeater power consumption by 70% while maintaining less than 1% spectral efficiency outage.
Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography-Based Motion Prediction Using Sliding-Window Normalization
Authors: Taichi Tanaka, Isao Nambu, Yasuhiro Wada
Date: 2025-04-04 00:00:00
Tasks: Classification, Domain Adaptation, Electromyography (EMG), Motion Estimation, motion prediction, Transfer Learning

Electromyography (EMG) signals are used in many applications, including prosthetic hands, assistive suits, and rehabilitation. Recent advances in motion estimation have improved performance, yet challenges remain in cross-subject generalization, electrode shift, and daily variations. When electrode shift occurs, both transfer learning and adversarial domain adaptation improve classification performance by reducing the performance gap to -1\% (eight-class scenario). However, additional data are needed for re-training in transfer learning or for training in adversarial domain adaptation. To address this issue, we investigated a sliding-window normalization (SWN) technique in a real-time prediction scenario. This method combines z-score normalization with a sliding-window approach to reduce the decline in classification performance caused by electrode shift. We validated the effectiveness of SWN using experimental data from a target trajectory tracking task involving the right arm. For three motions classification (rest, flexion, and extension of the elbow) obtained from EMG signals, our offline analysis showed that SWN reduced the differential classification accuracy to -1.0\%, representing a 6.6\% improvement compared to the case without normalization (-7.6\%). Furthermore, when SWN was combined with a strategy that uses a mixture of multiple electrode positions, classification accuracy improved by an additional 2.4\% over the baseline. These results suggest that SWN can effectively reduce the performance degradation caused by electrode shift, thereby enhancing the practicality of EMG-based motion estimation systems.
Single-Satellite Navigation on Lunar North Pole
Authors: Tim Gong, Andrew Dempster
Date: 2025-04-04 00:00:00
Tasks: Position

The Moon is a primary focus of space exploration. Current navigation methods face significant limitations in providing precise location data for lunar missions. In particular, existing methods often require direct Line of Sight to Earth, have limited capacity, and suffer from long signal travel times. This paper aims to tackle these challenges through a novel single satellite navigation system at the lunar North Pole. By utilising the Doppler effect, this system facilitates 3D geolocation of a stationary receiver on the lunar surface. Key findings include choosing a Low Lunar Orbit (LLO) suitable for North Pole coverage, designing a 3-step geolocation algorithm tailored to lunar conditions, constructing a comprehensive error budget, and evaluating the system performance through Dilution of Position (DOP).
An Algebraic Geometry Approach to Viewing Graph Solvability
Authors: Federica Arrigoni, Kathlén Kohn, Andrea Fusiello, Tomas Pajdla
Date: 2025-04-04 00:00:00

The concept of viewing graph solvability has gained significant interest in the context of structure-from-motion. A viewing graph is a mathematical structure where nodes are associated to cameras and edges represent the epipolar geometry connecting overlapping views. Solvability studies under which conditions the cameras are uniquely determined by the graph. In this paper we propose a novel framework for analyzing solvability problems based on Algebraic Geometry, demonstrating its potential in understanding structure-from-motion graphs and proving a conjecture that was previously proposed.
Roto-Translation Invariant Metrics on Position-Orientation Space
Authors: Gijs Bellaard, Bart M. N. Smets
Date: 2025-04-04 00:00:00
Tasks: Denoising, Position, Translation

Riemannian metrics on the position-orientation space M(3) that are roto-translation group SE(3) invariant play a key role in image analysis tasks like enhancement, denoising, and segmentation. These metrics enable roto-translation equivariant algorithms, with the associated Riemannian distance often used in implementation. However, computing the Riemannian distance is costly, which makes it unsuitable in situations where constant recomputation is needed. We propose the mav (minimal angular velocity) distance, defined as the Riemannian length of a geometrically meaningful curve, as a practical alternative. We see an application of the mav distance in geometric deep learning. Namely, neural networks architectures such as PONITA, relies on geometric invariants to create their roto-translation equivariant model. The mav distance offers a trainable invariant, with the parameters that determine the Riemannian metric acting as learnable weights. In this paper we: 1) classify and parametrize all SE(3) invariant metrics on M(3), 2) describes how to efficiently calculate the mav distance, and 3) investigate if including the mav distance within PONITA can positively impact its accuracy in predicting molecular properties.
Universal Collection of Euclidean Invariants between Pairs of Position-Orientations
Authors: Gijs Bellaard, Bart M. N. Smets, Remco Duits
Date: 2025-04-04 00:00:00
Tasks: Position

Euclidean E(3) equivariant neural networks that employ scalar fields on position-orientation space M(3) have been effectively applied to tasks such as predicting molecular dynamics and properties. To perform equivariant convolutional-like operations in these architectures one needs Euclidean invariant kernels on M(3) x M(3). In practice, a handcrafted collection of invariants is selected, and this collection is then fed into multilayer perceptrons to parametrize the kernels. We rigorously describe an optimal collection of 4 smooth scalar invariants on the whole of M(3) x M(3). With optimal we mean that the collection is independent and universal, meaning that all invariants are pertinent, and any invariant kernel is a function of them. We evaluate two collections of invariants, one universal and one not, using the PONITA neural network architecture. Our experiments show that using a collection of invariants that is universal positively impacts the accuracy of PONITA significantly.