pwc-archive/papers-with-abstracts huggingface dataset:
The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective
Authors: Tariq M Khan, Toufique Ahmed Soomro, Imran Razzak
Date: 2025-05-27 00:00:00
Tasks: Diagnostic, Sensitivity, Specificity

Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes and hypertension to Alzheimer's disease and cardiovascular disorders but current insights remain dispersed across platforms and specialties. Recent technological advances in optical coherence tomography (OCT/OCTA) and adaptive optics (AO) now deliver ultra-high-resolution scans (down to 5 {\mu}m ) with superior contrast and spatial integration, allowing early identification of microvascular abnormalities and neurodegenerative changes. At the same time, AI-driven and machine learning (ML) algorithms have revolutionized the analysis of large-scale retinal datasets, increasing sensitivity and specificity; for example, deep learning models achieve > 90 \% sensitivity for diabetic retinopathy and AUC = 0.89 for the prediction of cardiovascular risk from fundus photographs. The proliferation of mobile health technologies and telemedicine platforms further extends access, reduces costs, and facilitates community-based screening and longitudinal monitoring. Despite these breakthroughs, translation into routine practice is hindered by heterogeneous imaging protocols, limited external validation of AI models, and integration challenges within clinical workflows. In this review, we systematically synthesize the latest OCT/OCT and AO developments, AI/ML approaches, and mHealth/Tele-ophthalmology initiatives and quantify their diagnostic performance across disease domains. Finally, we propose a roadmap for multicenter protocol standardization, prospective validation trials, and seamless incorporation of retinal screening into primary and specialty care pathways-paving the way for precision prevention, early intervention, and ongoing treatment of life-threatening systemic diseases.
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
Authors: Nikola Andrejic, Milica Spasic, Igor Mihajlovic, Petra Milosavljevic, Djordje Pavlovic, Filip Milisavljevic, Uros Milivojevic, Danilo Delibasic, Ivana Mikic, Sinisa Todorovic
Date: 2025-05-27 00:00:00
Tasks: Domain Adaptation, Image-to-Image Translation, Nuclear Segmentation, Style Transfer, Translation

This work introduces Ui2i, a novel model for unpaired image-to-image translation, trained on content-wise unpaired datasets to enable style transfer across domains while preserving content. Building on CycleGAN, Ui2i incorporates key modifications to better disentangle content and style features, and preserve content integrity. Specifically, Ui2i employs U-Net-based generators with skip connections to propagate localized shallow features deep into the generator. Ui2i removes feature-based normalization layers from all modules and replaces them with approximate bidirectional spectral normalization -- a parameter-based alternative that enhances training stability. To further support content preservation, channel and spatial attention mechanisms are integrated into the generators. Training is facilitated through image scale augmentation. Evaluation on two biomedical tasks -- domain adaptation for nuclear segmentation in immunohistochemistry (IHC) images and unmixing of biological structures superimposed in single-channel immunofluorescence (IF) images -- demonstrates Ui2i's ability to preserve content fidelity in settings that demand more accurate structural preservation than typical translation tasks. To the best of our knowledge, Ui2i is the first approach capable of separating superimposed signals in IF images using real, unpaired training data.
A Feasibility Study of Task-Based fMRI at 0.55 T
Authors: Parsa Razmara, Takfarinas Medani, Anand A. Joshi, Majid Abbasi Sisara, Ye Tian, Sophia X. Cui, Justin P. Haldar, Krishna S. Nayak, Richard M. Leahy
Date: 2025-05-26 00:00:00
Tasks: Diagnostic, EEG

0.55T MRI offers advantages compared to conventional field strengths, including reduced susceptibility artifacts and better compatibility with simultaneous EEG recordings. However, reliable task-based fMRI at 0.55T has not been significantly demonstrated. In this study, we establish a robust task-based fMRI protocol and analysis pipeline at 0.55T that achieves full brain coverage and results comparable to what is expected for activation extent and location. We attempted fMRI at 0.55T by combining EPI acquisition with custom analysis techniques. Finger-tapping and visual tasks were used, comparing 5- and 10-minute runs to enhance activation detection. The results show significant activations, demonstrating that high-quality task-based fMRI is achievable at 0.55T in single subjects. This study demonstrates that reliable task-based fMRI is feasible on 0.55T scanners, potentially broadening functional neuroimaging access in clinical and research settings where high-field MRI is unavailable or impractical, supporting broader diagnostic and research applications.
DiffNMR: Advancing Inpainting of Randomly Sampled Nuclear Magnetic Resonance Signals
Authors: Sen Yan, Fabrizio Gabellieri, Etienne Goffinet, Filippo Castiglione, Thomas Launey
Date: 2025-05-26 00:00:00

Nuclear Magnetic Resonance (NMR) spectroscopy leverages nuclear magnetization to probe molecules' chemical environment, structure, and dynamics, with applications spanning from pharmaceuticals to the petroleum industry. Despite its utility, the high cost of NMR instrumentation, operation and the lengthy duration of experiments necessitate the development of computational techniques to optimize acquisition times. Non-Uniform sampling (NUS) is widely employed as a sub-sampling method to address these challenges, but it often introduces artifacts and degrades spectral quality, offsetting the benefits of reduced acquisition times. In this work, we propose the use of deep learning techniques to enhance the reconstruction quality of NUS spectra. Specifically, we explore the application of diffusion models, a relatively untapped approach in this domain. Our methodology involves applying diffusion models to both time-time and time-frequency NUS data, yielding satisfactory reconstructions of challenging spectra from the benchmark Artina dataset. This approach demonstrates the potential of diffusion models to improve the efficiency and accuracy of NMR spectroscopy as well as the superiority of using a time-frequency domain data over the time-time one, opening new landscapes for future studies.
Image denoising as a conditional expectation
Authors: Sajal Chakroborty, Suddhasattwa Das
Date: 2025-05-24 00:00:00
Tasks: Denoising, Image Denoising

All techniques for denoising involve a notion of a true (noise-free) image, and a hypothesis space. The hypothesis space may reconstruct the image directly as a grayscale valued function, or indirectly by its Fourier or wavelet spectrum. Most common techniques estimate the true image as a projection to some subspace. We propose an interpretation of a noisy image as a collection of samples drawn from a certain probability space. Within this interpretation, projection based approaches are not guaranteed to be unbiased and convergent. We present a data-driven denoising method in which the true image is recovered as a conditional expectation. Although the probability space is unknown apriori, integrals on this space can be estimated by kernel integral operators. The true image is reformulated as the least squares solution to a linear equation in a reproducing kernel Hilbert space (RKHS), and involving various kernel integral operators as linear transforms. Assuming the true image to be a continuous function on a compact planar domain, the technique is shown to be convergent as the number of pixels goes to infinity. We also show that for a picture with finite number of pixels, the convergence result can be used to choose the various parameters for an optimum denoising result.
SW-ViT: A Spatio-Temporal Vision Transformer Network with Post Denoiser for Sequential Multi-Push Ultrasound Shear Wave Elastography
Authors: Ahsan Habib Akash, MD Jahin Alam, Md. Kamrul Hasan
Date: 2025-05-24 00:00:00
Tasks: Denoising, Segmentation, SSIM

Objective: Ultrasound Shear Wave Elastography (SWE) demonstrates great potential in assessing soft-tissue pathology by mapping tissue stiffness, which is linked to malignancy. Traditional SWE methods have shown promise in estimating tissue elasticity, yet their susceptibility to noise interference, reliance on limited training data, and inability to generate segmentation masks concurrently present notable challenges to accuracy and reliability. Approach: In this paper, we propose SW-ViT, a novel two-stage deep learning framework for SWE that integrates a CNN-Spatio-Temporal Vision Transformer-based reconstruction network with an efficient Transformer-based post-denoising network. The first stage uses a 3D ResNet encoder with multi-resolution spatio-temporal Transformer blocks that capture spatial and temporal features, followed by a squeeze-and-excitation attention decoder that reconstructs 2D stiffness maps. To address data limitations, a patch-based training strategy is adopted for localized learning and reconstruction. In the second stage, a denoising network with a shared encoder and dual decoders processes inclusion and background regions to produce a refined stiffness map and segmentation mask. A hybrid loss combining regional, smoothness, fusion, and Intersection over Union (IoU) components ensures improvements in both reconstruction and segmentation. Results: On simulated data, our method achieves PSNR of 32.68 dB, CNR of 46.78 dB, and SSIM of 0.995. On phantom data, results include PSNR of 21.11 dB, CNR of 42.14 dB, and SSIM of 0.936. Segmentation IoU values reach 0.949 (simulation) and 0.738 (phantom) with ASSD values being 0.184 and 1.011, respectively. Significance: SW-ViT delivers robust, high-quality elasticity map estimates from noisy SWE data and holds clear promise for clinical application.
An Artificial Intelligence Model for Early Stage Breast Cancer Detection from Biopsy Images
Authors: Neil Chaudhary, Zaynah Dhunny
Date: 2025-05-24 00:00:00
Tasks: Breast Cancer Detection, Cancer Classification

Accurate identification of breast cancer types plays a critical role in guiding treatment decisions and improving patient outcomes. This paper presents an artificial intelligence enabled tool designed to aid in the identification of breast cancer types using histopathological biopsy images. Traditionally additional tests have to be done on women who are detected with breast cancer to find out the types of cancer it is to give the necessary cure. Those tests are not only invasive but also delay the initiation of treatment and increase patient burden. The proposed model utilizes a convolutional neural network (CNN) architecture to distinguish between benign and malignant tissues as well as accurate subclassification of breast cancer types. By preprocessing the images to reduce noise and enhance features, the model achieves reliable levels of classification performance. Experimental results on such datasets demonstrate the model's effectiveness, outperforming several existing solutions in terms of accuracy, precision, recall, and F1-score. The study emphasizes the potential of deep learning techniques in clinical diagnostics and offers a promising tool to assist pathologists in breast cancer classification.
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics
Authors: Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu
Date: 2025-05-23 00:00:00
Tasks: Image Compression

As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper takes a step forward in accelerating the training of LIC methods by modeling the neural training dynamics. We first propose a Sensitivity-aware True and Dummy Embedding Training mechanism (STDET) that clusters LIC model parameters into few separate modes where parameters are expressed as affine transformations of reference parameters within the same mode. By further utilizing the stable intra-mode correlations throughout training and parameter sensitivities, we gradually embed non-reference parameters, reducing the number of trainable parameters. Additionally, we incorporate a Sampling-then-Moving Average (SMA) technique, interpolating sampled weights from stochastic gradient descent (SGD) training to obtain the moving average weights, ensuring smooth temporal behavior and minimizing training state variances. Overall, our method significantly reduces training space dimensions and the number of trainable parameters without sacrificing model performance, thus accelerating model convergence. We also provide a theoretical analysis on the Noisy quadratic model, showing that the proposed method achieves a lower training variance than standard SGD. Our approach offers valuable insights for further developing efficient training methods for LICs.
A Foundation Model Framework for Multi-View MRI Classification of Extramural Vascular Invasion and Mesorectal Fascia Invasion in Rectal Cancer
Authors: Yumeng Zhang, Zohaib Salahuddin, Danial Khan, Shruti Atul Mali, Henry C. Woodruff, Sina Amirrajab, Eduardo Ibor-Crespo, Ana Jimenez-Pastor, Luis Marti-Bonmati, Philippe Lambin
Date: 2025-05-23 00:00:00
Tasks: Diagnostic, MRI classification

Background: Accurate MRI-based identification of extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) is pivotal for risk-stratified management of rectal cancer, yet visual assessment is subjective and vulnerable to inter-institutional variability. Purpose: To develop and externally evaluate a multicenter, foundation-model-driven framework that automatically classifies EVI and MFI on axial and sagittal T2-weighted MRI. Methods: This retrospective study used 331 pre-treatment rectal cancer MRI examinations from three European hospitals. After TotalSegmentator-guided rectal patch extraction, a self-supervised frequency-domain harmonization pipeline was trained to minimize scanner-related contrast shifts. Four classifiers were compared: ResNet50, SeResNet, the universal biomedical pretrained transformer (UMedPT) with a lightweight MLP head, and a logistic-regression variant using frozen UMedPT features (UMedPT_LR). Results: UMedPT_LR achieved the best EVI detection when axial and sagittal features were fused (AUC = 0.82; sensitivity = 0.75; F1 score = 0.73), surpassing the Chaimeleon Grand-Challenge winner (AUC = 0.74). The highest MFI performance was attained by UMedPT on axial harmonized images (AUC = 0.77), surpassing the Chaimeleon Grand-Challenge winner (AUC = 0.75). Frequency-domain harmonization improved MFI classification but variably affected EVI performance. Conventional CNNs (ResNet50, SeResNet) underperformed, especially in F1 score and balanced accuracy. Conclusion: These findings demonstrate that combining foundation model features, harmonization, and multi-view fusion significantly enhances diagnostic performance in rectal MRI.
High-Fidelity Functional Ultrasound Reconstruction via A Visual Auto-Regressive Framework
Authors: Xuhang Chen, Zhuo Li, Yanyan Shen, Mufti Mahmud, Hieu Pham, Chi-Man Pun, Shuqiang Wang
Date: 2025-05-23 00:00:00
Tasks: Diversity, Fairness

Functional ultrasound (fUS) imaging provides exceptional spatiotemporal resolution for neurovascular mapping, yet its practical application is significantly hampered by critical challenges. Foremost among these are data scarcity, arising from ethical considerations and signal degradation through the cranium, which collectively limit dataset diversity and compromise the fairness of downstream machine learning models.
Explainable Anatomy-Guided AI for Prostate MRI: Foundation Models and In Silico Clinical Trials for Virtual Biopsy-based Risk Assessment
Authors: Danial Khan, Zohaib Salahuddin, Yumeng Zhang, Sheng Kuang, Shruti Atul Mali, Henry C. Woodruff, Sina Amirrajab, Rachel Cavill, Eduardo Ibor-Crespo, Ana Jimenez-Pastor, Adrian Galiana-Bordera, Paula Jimenez Gomez, Luis Marti-Bonmati, Philippe Lambin
Date: 2025-05-23 00:00:00
Tasks: Anatomy, counterfactual, Diagnostic

We present a fully automated, anatomically guided deep learning pipeline for prostate cancer (PCa) risk stratification using routine MRI. The pipeline integrates three key components: an nnU-Net module for segmenting the prostate gland and its zones on axial T2-weighted MRI; a classification module based on the UMedPT Swin Transformer foundation model, fine-tuned on 3D patches with optional anatomical priors and clinical data; and a VAE-GAN framework for generating counterfactual heatmaps that localize decision-driving image regions. The system was developed using 1,500 PI-CAI cases for segmentation and 617 biparametric MRIs with metadata from the CHAIMELEON challenge for classification (split into 70% training, 10% validation, and 20% testing). Segmentation achieved mean Dice scores of 0.95 (gland), 0.94 (peripheral zone), and 0.92 (transition zone). Incorporating gland priors improved AUC from 0.69 to 0.72, with a three-scale ensemble achieving top performance (AUC = 0.79, composite score = 0.76), outperforming the 2024 CHAIMELEON challenge winners. Counterfactual heatmaps reliably highlighted lesions within segmented regions, enhancing model interpretability. In a prospective multi-center in-silico trial with 20 clinicians, AI assistance increased diagnostic accuracy from 0.72 to 0.77 and Cohen's kappa from 0.43 to 0.53, while reducing review time per case by 40%. These results demonstrate that anatomy-aware foundation models with counterfactual explainability can enable accurate, interpretable, and efficient PCa risk assessment, supporting their potential use as virtual biopsies in clinical practice.
Promptable cancer segmentation using minimal expert-curated data
Authors: Lynn Karam, Yipei Wang, Veeru Kasivisvanathan, Mirabela Rusu, Yipeng Hu, Shaheer U. Saeed
Date: 2025-05-23 00:00:00
Tasks: Diagnostic, Segmentation

Automated segmentation of cancer on medical images can aid targeted diagnostic and therapeutic procedures. However, its adoption is limited by the high cost of expert annotations required for training and inter-observer variability in datasets. While weakly-supervised methods mitigate some challenges, using binary histology labels for training as opposed to requiring full segmentation, they require large paired datasets of histology and images, which are difficult to curate. Similarly, promptable segmentation aims to allow segmentation with no re-training for new tasks at inference, however, existing models perform poorly on pathological regions, again necessitating large datasets for training. In this work we propose a novel approach for promptable segmentation requiring only 24 fully-segmented images, supplemented by 8 weakly-labelled images, for training. Curating this minimal data to a high standard is relatively feasible and thus issues with the cost and variability of obtaining labels can be mitigated. By leveraging two classifiers, one weakly-supervised and one fully-supervised, our method refines segmentation through a guided search process initiated by a single-point prompt. Our approach outperforms existing promptable segmentation methods, and performs comparably with fully-supervised methods, for the task of prostate cancer segmentation, while using substantially less annotated data (up to 100X less). This enables promptable segmentation with very minimal labelled data, such that the labels can be curated to a very high standard.
UltraBoneUDF: Self-supervised Bone Surface Reconstruction from Ultrasound Based on Neural Unsigned Distance Functions
Authors: Luohong Wu, Matthias Seibold, Nicola A. Cavalcanti, Giuseppe Loggia, Lisa Reissner, Bastian Sigrist, Jonas Hein, Lilian Calvet, Arnd Viehöfer, Philipp Fürnstahl
Date: 2025-05-23 00:00:00
Tasks: Surface Reconstruction

Background: Bone surface reconstruction plays a critical role in computer-assisted orthopedic surgery. Compared to traditional imaging modalities such as CT and MRI, ultrasound offers a radiation-free, cost-effective, and portable alternative. Continuous bone surface reconstruction can be employed for many clinical applications. However, due to the inherent limitations of ultrasound imaging, B-mode ultrasound typically capture only partial bone surfaces. Existing reconstruction methods struggle with such incomplete data, leading to artifacts and increased reconstruction errors. Effective techniques for accurately reconstructing thin and open bone surfaces from real-world 3D ultrasound volumes remain lacking. Methods: We propose UltraBoneUDF, a self-supervised framework designed for reconstructing open bone surfaces from ultrasound using neural Unsigned Distance Functions. To enhance reconstruction quality, we introduce a novel global feature extractor that effectively fuses ultrasound-specific image characteristics. Additionally, we present a novel loss function based on local tangent plane optimization that substantially improves surface reconstruction quality. UltraBoneUDF and baseline models are extensively evaluated on four open-source datasets. Results: Qualitative results highlight the limitations of the state-of-the-art methods for open bone surface reconstruction and demonstrate the effectiveness of UltraBoneUDF. Quantitatively, UltraBoneUDF significantly outperforms competing methods across all evaluated datasets for both open and closed bone surface reconstruction in terms of mean Chamfer distance error: 1.10 mm on the UltraBones100k dataset (39.6\% improvement compared to the SOTA), 0.23 mm on the OpenBoneCT dataset (69.3\% improvement), 0.18 mm on the ClosedBoneCT dataset (70.2\% improvement), and 0.05 mm on the Prostate dataset (55.3\% improvement).
Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection
Authors: Dan Yuan, Yi Feng, Ziyun Tang
Date: 2025-05-23 00:00:00
Tasks: Anatomy

Intraventricular hemorrhage (IVH) is a severe neurological complication among premature infants, necessitating early and accurate detection from brain ultrasound (US) images to improve clinical outcomes. While recent deep learning methods offer promise for computer-aided diagnosis, challenges remain in capturing both local spatial details and global contextual dependencies critical for segmenting brain anatomies. In this work, we propose an enhanced Residual U-Net architecture incorporating two complementary attention mechanisms: the Convolutional Block Attention Module (CBAM) and a Sparse Attention Layer (SAL). The CBAM improves the model's ability to refine spatial and channel-wise features, while the SAL introduces a dual-branch design, sparse attention filters out low-confidence query-key pairs to suppress noise, and dense attention ensures comprehensive information propagation. Extensive experiments on the Brain US dataset demonstrate that our method achieves state-of-the-art segmentation performance, with a Dice score of 89.04% and IoU of 81.84% for ventricle region segmentation. These results highlight the effectiveness of integrating spatial refinement and attention sparsity for robust brain anatomy detection. Code is available at: https://github.com/DanYuan001/BrainImgSegment.