pwc-archive/papers-with-abstracts huggingface dataset:
Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts
Authors: Lingyun Gao, Cristian Tejedor-Garcia, Catia Cucchiarini, Helmer Strik
Date: 2025-06-04 00:00:00
Tasks: Mistake Detection, speech-recognition, Speech Recognition

Automatic reading aloud evaluation can provide valuable support to teachers by enabling more efficient scoring of reading exercises. However, research on reading evaluation systems and applications remains limited. We present a novel multimodal approach that leverages audio and knowledge from text resources. In particular, we explored the potential of using Whisper and instruction-tuned large language models (LLMs) with prompts to improve transcriptions for child speech recognition, as well as their effectiveness in downstream reading mistake detection. Our results demonstrate the effectiveness of prompting Whisper and prompting LLM, compared to the baseline Whisper model without prompting. The best performing system achieved state-of-the-art recognition performance in Dutch child read speech, with a word error rate (WER) of 5.1%, improving the baseline WER of 9.4%. Furthermore, it significantly improved reading mistake detection, increasing the F1 score from 0.39 to 0.73.
Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity
Authors: Loann Peurey, Marvin Lavechin, Tarek Kunze, Manel Khentout, Lucas Gautheron, Emmanuel Dupoux, Alejandrina Cristia
Date: 2025-06-04 00:00:00
Tasks: Form

Audio-recordings collected with a child-worn device are a fundamental tool in child language research. Long-form recordings collected over whole days promise to capture children's input and production with minimal observer bias, and therefore high validity. The sheer volume of resulting data necessitates automated analysis to extract relevant metrics for researchers and clinicians. This paper summarizes collective knowledge on this technique, providing entry points to existing resources. We also highlight various sources of error that threaten the accuracy of automated annotations and the interpretation of resulting metrics. To address this, we propose potential troubleshooting metrics to help users assess data quality. While a fully automated quality control system is not feasible, we outline practical strategies for researchers to improve data collection and contextualize their analyses.
Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier
Authors: Tarek Kunze, Marianne Métais, Hadrien Titeux, Lucas Elbert, Joseph Coffey, Emmanuel Dupoux, Alejandrina Cristia, Marvin Lavechin
Date: 2025-06-04 00:00:00
Tasks: Blocking

Recordings gathered with child-worn devices promised to revolutionize both fundamental and applied speech sciences by allowing the effortless capture of children's naturalistic speech environment and language production. This promise hinges on speech technologies that can transform the sheer mounds of data thus collected into usable information. This paper demonstrates several obstacles blocking progress by summarizing three years' worth of experiments aimed at improving one fundamental task: Voice Type Classification. Our experiments suggest that improvements in representation features, architecture, and parameter search contribute to only marginal gains in performance. More progress is made by focusing on data relevance and quantity, which highlights the importance of collecting data with appropriate permissions to allow sharing.
Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Authors: Tahiya Chowdhury, Veronica Romero
Date: 2025-06-02 00:00:00
Tasks: Fairness

Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture behavioral variations equitably and can then contribute to bias. We evaluate speech features extracted from two widely used speech analysis tools, OpenSMILE and Praat, to assess their reliability when considering adolescents with autism. We observed considerable variation in features across tools, which influenced model performance across context and demographic groups. We encourage domain-relevant verification to enhance the reliability of machine learning models in clinical applications.
Embedded Acoustic Intelligence for Automotive Systems
Authors: Renjith Rajagopal, Peter Winzell, Sladjana Strbac, Konstantin Lindström, Petter Hörling, Faisal Kohestani, Niloofar Mehrzad
Date: 2025-06-02 00:00:00
Tasks: Autonomous Driving

Transforming sound insights into actionable streams of data, this abstract leverages findings from degree thesis research to enhance automotive system intelligence, enabling us to address road type [1].By extracting and interpreting acoustic signatures from microphones installed within the wheelbase of a car, we focus on classifying road type.Utilizing deep neural networks and feature extraction powered by pre-trained models from the Open AI ecosystem (via Hugging Face [2]), our approach enables Autonomous Driving and Advanced Driver- Assistance Systems (AD/ADAS) to anticipate road surfaces, support adaptive learning for active road noise cancellation, and generate valuable insights for urban planning. The results of this study were specifically captured to support a compelling business case for next-generation automotive systems. This forward-looking approach not only promises to redefine passenger comfort and improve vehicle safety, but also paves the way for intelligent, data-driven urban road management, making the future of mobility both achievable and sustainable.
Exploring the Design Space of 3D MLLMs for CT Report Generation
Authors: Mohammed Baharoon, Jun Ma, Congyu Fang, Augustin Toma, Bo wang
Date: 2025-06-26 00:00:00

Multimodal Large Language Models (MLLMs) have emerged as a promising way to automate Radiology Report Generation (RRG). In this work, we systematically investigate the design space of 3D MLLMs, including visual input representation, projectors, Large Language Models (LLMs), and fine-tuning techniques for 3D CT report generation. We also introduce two knowledge-based report augmentation methods that improve performance on the GREEN score by up to 10\%, achieving the 2nd place on the MICCAI 2024 AMOS-MM challenge. Our results on the 1,687 cases from the AMOS-MM dataset show that RRG is largely independent of the size of LLM under the same training protocol. We also show that larger volume size does not always improve performance if the original ViT was pre-trained on a smaller volume size. Lastly, we show that using a segmentation mask along with the CT volume improves performance. The code is publicly available at https://github.com/bowang-lab/AMOS-MM-Solution
Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising
Authors: Hojat Asgariandehkordi, Mostafa Sharifzadeh, Hassan Rivaz
Date: 2025-06-26 00:00:00
Tasks: Denoising

Ultrasound Coherent Plane Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While increasing the number of angles generally improves image quality, it drastically reduces the frame rate and can introduce blurring artifacts in fast-moving targets. Moreover, compounded images remain susceptible to noise, particularly when acquired with a limited number of transmissions. We propose a zero-shot denoising framework tailored for low-angle CPWC acquisitions, which enhances contrast without relying on a separate training dataset. The method divides the available transmission angles into two disjoint subsets, each used to form compound images that include higher noise levels. The new compounded images are then used to train a deep model via a self-supervised residual learning scheme, enabling it to suppress incoherent noise while preserving anatomical structures. Because angle-dependent artifacts vary between the subsets while the underlying tissue response is similar, this physics-informed pairing allows the network to learn to disentangle the inconsistent artifacts from the consistent tissue signal. Unlike supervised methods, our model requires no domain-specific fine-tuning or paired data, making it adaptable across anatomical regions and acquisition setups. The entire pipeline supports efficient training with low computational cost due to the use of a lightweight architecture, which comprises only two convolutional layers. Evaluations on simulation, phantom, and in vivo data demonstrate superior contrast enhancement and structure preservation compared to both classical and deep learning-based denoising methods.
Generalizable Neural Electromagnetic Inverse Scattering
Authors: Yizhe Cheng, Chunxun Tian, Haoru Wang, Wentao Zhu, Xiaoxuan Ma, Yizhou Wang
Date: 2025-06-26 00:00:00

Solving Electromagnetic Inverse Scattering Problems (EISP) is fundamental in applications such as medical imaging, where the goal is to reconstruct the relative permittivity from scattered electromagnetic field. This inverse process is inherently ill-posed and highly nonlinear, making it particularly challenging. A recent machine learning-based approach, Img-Interiors, shows promising results by leveraging continuous implicit functions. However, it requires case-specific optimization, lacks generalization to unseen data, and fails under sparse transmitter setups (e.g., with only one transmitter). To address these limitations, we revisit EISP from a physics-informed perspective, reformulating it as a two stage inverse transmission-scattering process. This formulation reveals the induced current as a generalizable intermediate representation, effectively decoupling the nonlinear scattering process from the ill-posed inverse problem. Built on this insight, we propose the first generalizable physics-driven framework for EISP, comprising a current estimator and a permittivity solver, working in an end-to-end manner. The current estimator explicitly learns the induced current as a physical bridge between the incident and scattered field, while the permittivity solver computes the relative permittivity directly from the estimated induced current. This design enables data-driven training and generalizable feed-forward prediction of relative permittivity on unseen data while maintaining strong robustness to transmitter sparsity. Extensive experiments show that our method outperforms state-of-the-art approaches in reconstruction accuracy, generalization, and robustness. This work offers a fundamentally new perspective on electromagnetic inverse scattering and represents a major step toward cost-effective practical solutions for electromagnetic imaging.
Semantic Scene Graph for Ultrasound Image Explanation and Scanning Guidance
Authors: Xuesong Li, Dianye Huang, Yameng Zhang, Nassir Navab, Zhongliang Jiang
Date: 2025-06-24 00:00:00

Understanding medical ultrasound imaging remains a long-standing challenge due to significant visual variability caused by differences in imaging and acquisition parameters. Recent advancements in large language models (LLMs) have been used to automatically generate terminology-rich summaries orientated to clinicians with sufficient physiological knowledge. Nevertheless, the increasing demand for improved ultrasound interpretability and basic scanning guidance among non-expert users, e.g., in point-of-care settings, has not yet been explored. In this study, we first introduce the scene graph (SG) for ultrasound images to explain image content to ordinary and provide guidance for ultrasound scanning. The ultrasound SG is first computed using a transformer-based one-stage method, eliminating the need for explicit object detection. To generate a graspable image explanation for ordinary, the user query is then used to further refine the abstract SG representation through LLMs. Additionally, the predicted SG is explored for its potential in guiding ultrasound scanning toward missing anatomies within the current imaging view, assisting ordinary users in achieving more standardized and complete anatomical exploration. The effectiveness of this SG-based image explanation and scanning guidance has been validated on images from the left and right neck regions, including the carotid and thyroid, across five volunteers. The results demonstrate the potential of the method to maximally democratize ultrasound by enhancing its interpretability and usability for ordinaries.
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
Authors: Qifei Cui, Xinyu Lu
Date: 2025-06-26 00:00:00
Tasks: Anomaly Detection, Brain Tumor Segmentation, Image Augmentation, Sensitivity, Tumor Segmentation

This work introduces a novel framework for brain tumor segmentation leveraging pre-trained GANs and Unet architectures. By combining a global anomaly detection module with a refined mask generation network, the proposed model accurately identifies tumor-sensitive regions and iteratively enhances segmentation precision using adversarial loss constraints. Multi-modal MRI data and synthetic image augmentation are employed to improve robustness and address the challenge of limited annotated datasets. Experimental results on the BraTS dataset demonstrate the effectiveness of the approach, achieving high sensitivity and accuracy in both lesion-wise Dice and HD95 metrics than the baseline. This scalable method minimizes the dependency on fully annotated data, paving the way for practical real-world applications in clinical settings.
Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations
Authors: Jing Yang, Qunliang Xing, Mai Xu, Minglang Qiao
Date: 2025-06-26 00:00:00
Tasks: Data Compression

Joint Photographic Experts Group (JPEG) achieves data compression by quantizing Discrete Cosine Transform (DCT) coefficients, which inevitably introduces compression artifacts. Most existing JPEG quality enhancement methods operate in the pixel domain, suffering from the high computational costs of decoding. Consequently, direct enhancement of JPEG images in the DCT domain has gained increasing attention. However, current DCT-domain methods often exhibit limited performance. To address this challenge, we identify two critical types of correlations within the DCT coefficients of JPEG images. Building on this insight, we propose an Advanced DCT-domain JPEG Quality Enhancement (AJQE) method that fully exploits these correlations. The AJQE method enables the adaptation of numerous well-established pixel-domain models to the DCT domain, achieving superior performance with reduced computational complexity. Compared to the pixel-domain counterparts, the DCT-domain models derived by our method demonstrate a 0.35 dB improvement in PSNR and a 60.5% increase in enhancement throughput on average.
A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
Authors: Shuwei Xing, Derek W. Cool, David Tessier, Elvis C. S. Chen, Terry M. Peters, Aaron Fenster
Date: 2025-06-26 00:00:00

3D ultrasound (US) imaging has shown significant benefits in enhancing the outcomes of percutaneous liver tumour ablation. Its clinical integration is crucial for transitioning 3D US into the therapeutic domain. However, challenges of tumour identification in US images continue to hinder its broader adoption. In this work, we propose a novel framework for integrating 3D US into the standard ablation workflow. We present a key component, a clinically viable 2D US-CT/MRI registration approach, leveraging 3D US as an intermediary to reduce registration complexity. To facilitate efficient verification of the registration workflow, we also propose an intuitive multimodal image visualization technique. In our study, 2D US-CT/MRI registration achieved a landmark distance error of approximately 2-4 mm with a runtime of 0.22s per image pair. Additionally, non-rigid registration reduced the mean alignment error by approximately 40% compared to rigid registration. Results demonstrated the efficacy of the proposed 2D US-CT/MRI registration workflow. Our integration framework advanced the capabilities of 3D US imaging in improving percutaneous tumour ablation, demonstrating the potential to expand the therapeutic role of 3D US in clinical interventions.
Development of MR spectral analysis method robust against static magnetic field inhomogeneity
Authors: Shuki Maruyama, Hidenori Takeshima
Date: 2025-06-26 00:00:00

Purpose:To develop a method that enhances the accuracy of spectral analysis in the presence of static magnetic field B0 inhomogeneity. Methods:The authors proposed a new spectral analysis method utilizing a deep learning model trained on modeled spectra that consistently represent the spectral variations induced by B0 inhomogeneity. These modeled spectra were generated from the B0 map and metabolite ratios of the healthy human brain. The B0 map was divided into a patch size of subregions, and the separately estimated metabolites and baseline components were averaged and then integrated. The quality of the modeled spectra was visually and quantitatively evaluated against the measured spectra. The analysis models were trained using measured, simulated, and modeled spectra. The performance of the proposed method was assessed using mean squared errors (MSEs) of metabolite ratios. The mean absolute percentage errors (MAPEs) of the metabolite ratios were also compared to LCModel when analyzing the phantom spectra acquired under two types of B0 inhomogeneity. Results:The modeled spectra exhibited broadened and narrowed spectral peaks depending on the B0 inhomogeneity and were quantitatively close to the measured spectra. The analysis model trained using measured spectra with modeled spectra improved MSEs by 49.89% compared to that trained using measured spectra alone, and by 26.66% compared to that trained using measured spectra with simulated spectra. The performance improved as the number of modeled spectra increased from 0 to 1,000. This model showed significantly lower MAPEs than LCModel under both types of B0 inhomogeneity. Conclusion:A new spectral analysis-trained deep learning model using the modeled spectra was developed. The results suggest that the proposed method has the potential to improve the accuracy of spectral analysis by increasing the training samples of spectra.
DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT
Authors: Shreeram Athreya, Carlos Olivares, Ameera Ismail, Kambiz Nael, William Speier, Corey Arnold
Date: 2025-06-20 00:00:00

Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, defined by persistent microvascular hypoperfusion that undermines tissue recovery and worsens clinical outcomes. Although prompt identification is crucial, standard clinical practice relies on perfusion magnetic resonance imaging (MRI) within 24 hours post-procedure, delaying intervention. In this work, we introduce the first-ever machine learning (ML) framework to predict no-reflow immediately after EVT by leveraging previously unexplored intra-procedural digital subtraction angiography (DSA) sequences and clinical variables. Our retrospective analysis included AIS patients treated at UCLA Medical Center (2011-2024) who achieved favorable mTICI scores (2b-3) and underwent pre- and post-procedure MRI. No-reflow was defined as persistent hypoperfusion (Tmax > 6 s) on post-procedural imaging. From DSA sequences (AP and lateral views), we extracted statistical and temporal perfusion features from the target downstream territory to train ML classifiers for predicting no-reflow. Our novel method significantly outperformed a clinical-features baseline(AUC: 0.7703 $\pm$ 0.12 vs. 0.5728 $\pm$ 0.12; accuracy: 0.8125 $\pm$ 0.10 vs. 0.6331 $\pm$ 0.09), demonstrating that real-time DSA perfusion dynamics encode critical insights into microvascular integrity. This approach establishes a foundation for immediate, accurate no-reflow prediction, enabling clinicians to proactively manage high-risk patients without reliance on delayed imaging.
Papanicolaou Stain Unmixing for RGB Image Using Weighted Nucleus Sparsity and Total Variation Regularization
Authors: Nanxin Gong, Saori Takeyama, Masahiro Yamaguchi, Takumi Urata, Fumikazu Kimura, Keiko Ishii
Date: 2025-06-25 00:00:00

The Papanicolaou stain, consisting of eosin Y, hematoxylin, light Green SF yellowish, orange G, and Bismarck brown Y, provides extensive color information essential for cervical cancer screening in cytopathology. However, the visual observation of these colors is subjective and difficult to characterize. In digital image analysis, the RGB intensities are affected by staining and imaging variations, hindering direct quantification of color in Papanicolaou-stained samples. Stain unmixing is a promising alternative that quantifies the amounts of dyes. In previous work, multispectral imaging was utilized to estimate the dye amounts of Papanicolaou stain for quantitative diagnosis. Still, its application to RGB images presents a challenge since the number of dyes exceeds the three RGB channels. This paper proposes a novel Papanicolaou stain unmixing method for RGB images that incorporates three key assumptions: nonnegative stain abundances; a sparse spatial distribution of hematoxylin, which binds to nuclei; and piecewise smoothness of stain abundances. By formulating this as an optimization problem with nonnegativity, weighted nucleus sparsity, and total variation regularizations, our method achieved excellent performance in stain quantification when validated against the results of multispectral imaging. We also adopted the proposed method for discriminating lobular endocervical glandular hyperplasia (LEGH), a precancerous lesion of gastric-type adenocarcinoma of the cervix. The resulting quantification distinctly characterized differences between LEGH and normal endocervical cells with stain abundance, and a classifier based on the quantification results achieved 98.0% accuracy. This demonstrates the significant potential of RGB-based stain unmixing for quantitative diagnosis.
Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
Authors: Caden Sweeney, Du Yong Kim, Branko Ristic, Brian Cheung
Date: 2025-06-25 00:00:00

Multi-target tracking in the maritime domain is a challenging problem due to the non-Gaussian and fluctuating characteristics of sea clutter. This article investigates the use of machine learning (ML) to the detection and tracking of low SIR targets in the maritime domain. The proposed method uses a transformer to extract point measurements from range-azimuth maps, before clustering and tracking using the Labelled mulit- Bernoulli (LMB) filter. A measurement driven birth density design based on the transformer attention maps is also developed. The error performance of the transformer based approach is presented and compared with a constant false alarm rate (CFAR) detection technique. The LMB filter is run in two scenarios, an ideal birth approach, and the measurement driven birth approach. Experiments indicate that the transformer based method has superior performance to the CFAR approach for all target scenarios discussed