pwc-archive/papers-with-abstracts huggingface dataset:
Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning
Authors: Yao Du, Jiaxin Zhuang, Xiaoyu Zheng, Jing Cong, Limei Guo, Chao He, Lin Luo, Xiaomeng Li
Date: 2025-03-05 00:00:00
Tasks: Diagnostic, Representation Learning, Self-Supervised Learning

Histopathology image analysis is fundamental to digital pathology, with hematoxylin and eosin (H&E) staining as the gold standard for diagnostic and prognostic assessments. While H&E imaging effectively highlights cellular and tissue structures, it lacks sensitivity to birefringence and tissue anisotropy, which are crucial for assessing collagen organization, fiber alignment, and microstructural alterations--key indicators of tumor progression, fibrosis, and other pathological conditions. To bridge this gap, we propose PolarHE, a dual modality fusion framework that integrates H&E with polarization imaging, leveraging the polarization ability to enhance tissue characterization. Our approach employs a feature decomposition strategy to disentangle common and modality specific features, ensuring effective multimodal representation learning. Through comprehensive validation, our approach significantly outperforms previous methods, achieving an accuracy of 86.70% on the Chaoyang dataset and 89.06% on the MHIST dataset. Moreover, polarization property visualization reveals distinct optical signatures of pathological tissues, highlighting its diagnostic potential. t-SNE visualizations further confirm our model effectively captures both shared and unique modality features, reinforcing the complementary nature of polarization imaging. These results demonstrate that polarization imaging is a powerful and underutilized modality in computational pathology, enriching feature representation and improving diagnostic accuracy. PolarHE establishes a promising direction for multimodal learning, paving the way for more interpretable and generalizable pathology models. Our code will be released after paper acceptance.
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
Authors: Zanting Ye, Xiaolong Niu, Xuanbin Wu, Wantong Lu, Lijun Lu
Date: 2025-03-05 00:00:00
Tasks: Computed Tomography (CT), Denoising, Organ Segmentation, Segmentation

Organ segmentation in Positron Emission Tomography (PET) plays a vital role in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by reducing radiation exposure. However, the inherent noise and blurred boundaries make organ segmentation more challenging. Additionally, existing PET organ segmentation methods rely on co-registered Computed Tomography (CT) annotations, overlooking the problem of modality mismatch. In this study, we propose LDOS, a novel CT-free ultra-LDPET organ segmentation pipeline. Inspired by Masked Autoencoders (MAE), we reinterpret LDPET as a naturally masked version of Full-Dose PET (FDPET). LDOS adopts a simple yet effective architecture: a shared encoder extracts generalized features, while task-specific decoders independently refine outputs for denoising and segmentation. By integrating CT-derived organ annotations into the denoising process, LDOS improves anatomical boundary recognition and alleviates the PET/CT misalignments. Experiments demonstrate that LDOS achieves state-of-the-art performance with mean Dice scores of 73.11% (18F-FDG) and 73.97% (68Ga-FAPI) across 18 organs in 5% dose PET. Our code is publicly available.
Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model
Authors: Steve Andreas Immanuel, Woojin Cho, Junhyuk Heo, Darongsae Kwon
Date: 2025-03-05 00:00:00
Tasks: Image Inpainting, Segmentation

Limited data is a common problem in remote sensing due to the high cost of obtaining annotated samples. In the few-shot segmentation task, models are typically trained on base classes with abundant annotations and later adapted to novel classes with limited examples. However, this often necessitates specialized model architectures or complex training strategies. Instead, we propose a simple approach that leverages diffusion models to generate diverse variations of novel-class objects within a given scene, conditioned by the limited examples of the novel classes. By framing the problem as an image inpainting task, we synthesize plausible instances of novel classes under various environments, effectively increasing the number of samples for the novel classes and mitigating overfitting. The generated samples are then assessed using a cosine similarity metric to ensure semantic consistency with the novel classes. Additionally, we employ Segment Anything Model (SAM) to segment the generated samples and obtain precise annotations. By using high-quality synthetic data, we can directly fine-tune off-the-shelf segmentation models. Experimental results demonstrate that our method significantly enhances segmentation performance in low-data regimes, highlighting its potential for real-world remote sensing applications.
RGB-Thermal Infrared Fusion for Robust Depth Estimation in Complex Environments
Authors: Zelin Meng, Takanori Fukao
Date: 2025-03-05 00:00:00
Tasks: Autonomous Driving, Depth Estimation

Depth estimation in complex real-world scenarios is a challenging task, especially when relying solely on a single modality such as visible light or thermal infrared (THR) imagery. This paper proposes a novel multimodal depth estimation model, RTFusion, which enhances depth estimation accuracy and robustness by integrating the complementary strengths of RGB and THR data. The RGB modality provides rich texture and color information, while the THR modality captures thermal patterns, ensuring stability under adverse lighting conditions such as extreme illumination. The model incorporates a unique fusion mechanism, EGFusion, consisting of the Mutual Complementary Attention (MCA) module for cross-modal feature alignment and the Edge Saliency Enhancement Module (ESEM) to improve edge detail preservation. Comprehensive experiments on the MS2 and ViViD++ datasets demonstrate that the proposed model consistently produces high-quality depth maps across various challenging environments, including nighttime, rainy, and high-glare conditions. The experimental results highlight the potential of the proposed method in applications requiring reliable depth estimation, such as autonomous driving, robotics, and augmented reality.
ReRAW: RGB-to-RAW Image Reconstruction via Stratified Sampling for Efficient Object Detection on the Edge
Authors: Radu Berdan, Beril Besbinar, Christoph Reinders, Junji Otsuka, Daisuke Iso
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Reconstruction, object-detection, Object Detection

Edge-based computer vision models running on compact, resource-limited devices benefit greatly from using unprocessed, detail-rich RAW sensor data instead of processed RGB images. Training these models, however, necessitates large labeled RAW datasets, which are costly and often impractical to obtain. Thus, converting existing labeled RGB datasets into sensor-specific RAW images becomes crucial for effective model training. In this paper, we introduce ReRAW, an RGB-to-RAW conversion model that achieves state-of-the-art reconstruction performance across five diverse RAW datasets. This is accomplished through ReRAW's novel multi-head architecture predicting RAW image candidates in gamma space. The performance is further boosted by a stratified sampling-based training data selection heuristic, which helps the model better reconstruct brighter RAW pixels. We finally demonstrate that pretraining compact models on a combination of high-quality synthetic RAW datasets (such as generated by ReRAW) and ground-truth RAW images for downstream tasks like object detection, outperforms both standard RGB pipelines, and RAW fine-tuning of RGB-pretrained models for the same task.
Technical report of a DMD-based Characterization Method for Vision Sensors
Authors: Yapeng Meng, Taoyi Wang, Yihan Lin
Date: 2025-03-04 00:00:00
Tasks: Benchmarking, Dataset Generation, Event-based vision

This technical report presents a novel DMD-based characterization method for vision sensors, particularly neuromorphic sensors such as event-based vision sensors (EVS) and Tianmouc, a complementary vision sensor. Traditional image sensor characterization standards, such as EMVA1288, are unsuitable for BVS due to their dynamic response characteristics. To address this, we propose a high-speed, high-precision testing system using a Digital Micromirror Device (DMD) to modulate spatial and temporal light intensity. This approach enables quantitative analysis of key parameters such as event latency, signal-to-noise ratio (SNR), and dynamic range (DR) under controlled conditions. Our method provides a standardized and reproducible testing framework, overcoming the limitations of existing evaluation techniques for neuromorphic sensors. Furthermore, we discuss the potential of this method for large-scale BVS dataset generation and conversion, paving the way for more consistent benchmarking of bio-inspired vision technologies.
Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images
Authors: Zhongyi Shui, Ruizhe Guo, Honglin Li, Yuxuan Sun, Yunlong Zhang, Chenglu Zhu, Jiatong Cai, Pingyi Chen, Yanzhou Su, Lin Yang
Date: 2025-03-04 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation, whole slide images

Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. Current approaches for nucleus detection in gigapixel WSIs utilize a sliding window methodology, which overlooks boarder contextual information (eg, tissue structure) and easily leads to inaccurate predictions. To address this problem, recent studies additionally crops a large Filed-of-View (FoV) region around each sliding window to extract contextual features. However, such methods substantially increases the inference latency. In this paper, we propose an effective and efficient context-aware nucleus detection algorithm. Specifically, instead of leveraging large FoV regions, we aggregate contextual clues from off-the-shelf features of historically visited sliding windows. This design greatly reduces computational overhead. Moreover, compared to large FoV regions at a low magnification, the sliding window patches have higher magnification and provide finer-grained tissue details, thereby enhancing the detection accuracy. To further improve the efficiency, we propose a grid pooling technique to compress dense feature maps of each patch into a few contextual tokens. Finally, we craft OCELOT-seg, the first benchmark dedicated to context-aware nucleus instance segmentation. Code, dataset, and model checkpoints will be available at https://github.com/windygoo/PathContext.
Illuminant and light direction estimation using Wasserstein distance method
Authors: Selcuk Yazar
Date: 2025-03-03 00:00:00
Tasks: Image Quality Assessment, object-detection, Object Detection

Illumination estimation remains a pivotal challenge in image processing, particularly for robotics, where robust environmental perception is essential under varying lighting conditions. Traditional approaches, such as RGB histograms and GIST descriptors, often fail in complex scenarios due to their sensitivity to illumination changes. This study introduces a novel method utilizing the Wasserstein distance, rooted in optimal transport theory, to estimate illuminant and light direction in images. Experiments on diverse images indoor scenes, black-and-white photographs, and night images demonstrate the method's efficacy in detecting dominant light sources and estimating their directions, outperforming traditional statistical methods in complex lighting environments. The approach shows promise for applications in light source localization, image quality assessment, and object detection enhancement. Future research may explore adaptive thresholding and integrate gradient analysis to enhance accuracy, offering a scalable solution for real-world illumination challenges in robotics and beyond.
Joint Sampling Frequency Offset Estimation and Compensation Based on the Farrow Structure
Authors: Deijany Rodriguez Linares, Oksana Moryakova, Håkan Johansson
Date: 2025-03-10 00:00:00

This paper introduces a sampling frequency offset (SFO) estimation method based on the Farrow structure, which is typically utilized for the SFO compensation and thereby enables a reduction of the implementation complexity of the SFO estimation. The proposed method is implemented in the time domain and works for arbitrary bandlimited signals, thus with no additional constraints on the waveform structure. Moreover, it can operate on only the real or imaginary part of a complex signal, which further reduces the estimation complexity. Furthermore, the proposed method can simultaneously estimate the SFO and additional sampling time offset (STO) and it is insensitive to other synchronization errors, like carrier frequency offset. Both the derivations of the proposed method and its implementation are presented, and through simulation examples, it is demonstrated that it can accurately estimate both SFO and STO for different types of bandlimited signals.
Utilizing High Sampling Rate ADCs for Cost Efficient MIMO Radios
Authors: Agrim Gupta, Shenggang Dong, Mehmet Mert Sahin, Younghan Nam, Frederik J. Harris, Dinesh Bharadia
Date: 2025-03-10 00:00:00

In the past decade, $>$1 Gsps ADCs have become commonplace and are used in many modern 5G base station chips. A major driving force behind this adoption is the benefits of digital up/down-conversion and improved digital filtering. Recent works have also advocated for utilizing this high sampling bandwidth to fit-in multiple MIMO streams, and reduce the number of ADCs required to build MIMO base-stations. This can potentially reduce the cost of Massive MIMO RUs, since ADCs are the most expensive electronics in the base-station radio chain. However, these recent works do not model the necessary decimation filters that exist in the signal path of these high sampling rate ADCs. We show in this short paper that because of the decimation filters, there can be introduction of cross-talks which can hinder the performance of these shared ADC interfaces. We simulate the shared ADC interface with Matlab 5G toolbox for uplink MIMO, and show that these cross-talks can be mitigated by performing MMSE equalization atop the PUSCH estimated channels.
Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds
Authors: Riccardo Mazzieri, Jacopo Pegoraro, Michele Rossi
Date: 2025-03-10 00:00:00
Tasks: Edge-computing, Gait Recognition, Novelty Detection, Privacy Preserving

The adoption of Millimeter-Wave (mmWave) radar devices for human sensing, particularly gait recognition, has recently gathered significant attention due to their efficiency, resilience to environmental conditions, and privacy-preserving nature. In this work, we tackle the challenging problem of Open-set Gait Recognition (OSGR) from sparse mmWave radar point clouds. Unlike most existing research, which assumes a closed-set scenario, our work considers the more realistic open-set case, where unknown subjects might be present at inference time, and should be correctly recognized by the system. Point clouds are well-suited for edge computing applications with resource constraints, but are more significantly affected by noise and random fluctuations than other representations, like the more common micro-Doppler signature. This is the first work addressing open-set gait recognition with sparse point cloud data. To do so, we propose a novel neural network architecture that combines supervised classification with unsupervised reconstruction of the point clouds, creating a robust, rich, and highly regularized latent space of gait features. To detect unknown subjects at inference time, we introduce a probabilistic novelty detection algorithm that leverages the structured latent space and offers a tunable trade-off between inference speed and prediction accuracy. Along with this paper, we release mmGait10, an original human gait dataset featuring over five hours of measurements from ten subjects, under varied walking modalities. Extensive experimental results show that our solution attains F1-Score improvements by 24% over state-of-the-art methods, on average, and across multiple openness levels.
ECNN: A Low-complex, Adjustable CNN for Industrial Pump Monitoring Using Vibration Data
Authors: Jonas Ney, Norbert Wehn
Date: 2025-03-10 00:00:00
Tasks: Anomaly Detection

Industrial pumps are essential components in various sectors, such as manufacturing, energy production, and water treatment, where their failures can cause significant financial and safety risks. Anomaly detection can be used to reduce those risks and increase reliability. In this work, we propose a novel enhanced convolutional neural network (ECNN) to predict the failure of an industrial pump based on the vibration data captured by an acceleration sensor. The convolutional neural network (CNN) is designed with a focus on low complexity to enable its implementation on edge devices with limited computational resources. Therefore, a detailed design space exploration is performed to find a topology satisfying the trade-off between complexity and accuracy. Moreover, to allow for adaptation to unknown pumps, our algorithm features a pump-specific parameter that can be determined by a small set of normal data samples. Finally, we combine the ECNN with a threshold approach to further increase the performance and satisfy the application requirements. As a result, our combined approach significantly outperforms a traditional statistical approach and a classical CNN in terms of accuracy. To summarize, this work provides a novel, low-complex, CNN-based algorithm that is enhanced by classical methods to offer high accuracy for anomaly detection of industrial pumps.
Secure Wireless-Powered zeRIS Communications
Authors: Jingyu Chen, Kunrui Cao, Panagiotis D. Diamantoulakis, Lu Lv, Liang Yang, Haolian Chi, Haiyang Ding
Date: 2025-03-10 00:00:00

This paper introduces the concept of wireless-powered zero-energy reconfigurable intelligent surface (zeRIS), and investigates a wireless-powered zeRIS aided communication system in terms of security, reliability and energy efficiency. In particular, we propose three new wireless-powered zeRIS modes: 1) in mode-I, N reconfigurable reflecting elements are adjusted to the optimal phase shift design of information user to maximize the reliability of the system; 2) in mode-II, N reconfigurable reflecting elements are adjusted to the optimal phase shift design of cooperative jamming user to maximize the security of the system; 3) in mode-III, N1 and N2 (N1+N2=N) reconfigurable reflecting elements are respectively adjusted to the optimal phase shift designs of information user and cooperative jamming user to balance the reliability and security of the system. Then, we propose three new metrics, i.e., joint outage probability (JOP), joint intercept probability (JIP), and secrecy energy efficiency (SEE), and analyze their closed-form expressions in three modes, respectively. The results show that under high transmission power, all the diversity gains of three modes are 1, and the JOPs of mode-I, mode-II and mode-III are improved by increasing the number of zeRIS elements, which are related to N2, N, and N^2_1, respectively. In addition, mode-I achieves the best JOP, while mode-II achieves the best JIP among three modes. We exploit two security-reliability trade-off (SRT) metrics, i.e., JOP versus JIP, and normalized joint intercept and outage probability (JIOP), to reveal the SRT performance of the proposed three modes. It is obtained that mode-II outperforms the other two modes in the JOP versus JIP, while mode-III and mode-II achieve the best performance of normalized JIOP at low and high transmission power, respectively.
Aliased Time-Modulated Array OFDM System
Authors: Marcin Wachowiak, André Bourdoux, Sofie Pollin
Date: 2025-03-10 00:00:00

The time-modulated array is a simple array architecture in which each antenna is connected to an RF switch that serves as a modulator. The phase shift is achieved by digitally controlling the relative delay between the periodic modulating sequences of the antennas. Two factors limit the practical use of this architecture. First, the switching frequency is high, as it must be a multiple of the sampling frequency. Second, the discrete modulating sequence introduces undesired harmonic replicas of the signal with non-negligible power. This paper exploits aliasing to simultaneously reduce sideband radiation and switching frequency. The transmit signal has a repeated block structure in the frequency domain to facilitate coherent combining of the aliased signal blocks. As a result, a factor $A$ reduction in switching frequency is achieved at the cost of a factor $A$ reduction in communication capacity. Doubling $A$ reduces sideband radiation by around 2.9 dB. The feasibility of the proposed method is experimentally validated for wideband signals.
Frequency Diverse Array OFDM Transmit System with Partial Overlap in Frequency
Authors: Marcin Wachowiak, André Bourdoux, Sofie Pollin
Date: 2025-03-10 00:00:00

Frequency-diverse array (FDA) is an alternative array architecture in which each antenna is preceded by a mixer instead of a phase shifter. The mixers introduce a frequency offset between signals transmitted by each antenna resulting in a time-varying beam pattern. However, time-dependent beamforming is not desirable for communication or sensing. In this paper, the FDA is combined with orthogonal frequency-division multiplexing (OFDM) modulation. The proposed beamforming method splits the OFDM symbol transmitted by all antennas into subcarrier blocks, which are precoded differently. The selected frequency offset between the antennas results in overlap and coherent summation of the differently precoded subcarrier blocks. This allows to achieve fully digital beamforming over a single block with the use of a single digital-to-analog converter. The system's joint communication and sensing performance is evaluated and sensitivity to errors is studied.
Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar
Authors: Hui Tang, Zhan Yang, Yu Rong, Li Chai
Date: 2025-03-10 00:00:00
Tasks: Computational Efficiency, Heart rate estimation, Management

Heart rate (HR) monitoring is crucial for assessing physical fitness, cardiovascular health, and stress management. Millimeter-wave radar offers a promising noncontact solution for long-term monitoring. However, accurate HR estimation remains challenging in low signal-tonoise ratio (SNR) conditions. To deal with both respiration harmonics and intermodulation interference, this paper proposes a cancellation-before-estimation strategy. Firstly, we present the adaptive extensive cancellation algorithm (ECA) to suppress respiratory and its low-order harmonics. Then, we propose an adaptive harmonic enhanced trace (AHET) method to avoid intermodulation interference by refining the HR search region. Various experimental results validate the effectiveness of the proposed methods, demonstrating improvements in accuracy, robustness, and computational efficiency compared to conventional approaches based on the FMCW (Frequency Modulated Continuous Wave) system