pwc-archive/papers-with-abstracts huggingface dataset:
TRUST -- Transformer-Driven U-Net for Sparse Target Recovery
Authors: Di An, Dylan Poppert, Jiayue Li, Mark Foster, Trac D. Tran
Date: 2025-06-01 00:00:00
Tasks: Decoder, Hallucination, SSIM

In the context of inverse problems $\bf y = Ax$, sparse recovery offers a powerful paradigm shift by enabling the stable solution of ill-posed or underdetermined systems through the exploitation of structure, particularly sparsity. Sparse regularization techniques via $\ell_0$- or $\ell_1$-norm minimization encourage solutions $\bf x$ that are both consistent with observations $\bf y$ and parsimonious in representation, often yielding physically meaningful interpretations. In this work, we address the classical inverse problem under the challenging condition where the sensing operator $\bf A$ is unknown and only a limited set of observation-target pairs $\{ \bf x,\bf y \}$ is available. We propose a novel neural architecture, TRUST, that integrates the attention mechanism of Transformers with the decoder pathway of a UNet to simultaneously learn the sensing operator and reconstruct the sparse signal. The TRUST model incorporates a Transformer-based encoding branch to capture long-range dependencies and estimate sparse support, which then guides a U-Net-style decoder to refine reconstruction through multiscale feature integration. The skip connections between the transformer stages and the decoder not only enhance image quality but also enable the decoder to access image features at different levels of abstraction. This hybrid architecture enables more accurate and robust recovery by combining global context with local details. Experimental results demonstrate that TRUST significantly outperforms traditional sparse recovery methods and standalone U-Net models, achieving superior performance in SSIM and PSNR metrics while effectively suppressing hallucination artifacts that commonly plague deep learning-based inverse solvers.
Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
Authors: Javier Salazar Cavazos, Scott Peltier
Date: 2025-06-01 00:00:00

Previous works in the literature apply 3D spatial-only models on 4D functional MRI data leading to possible sub-par feature extraction to be used for downstream tasks like classification. In this work, we aim to develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels that not only learn spatial information but in addition also capture temporal dynamics. Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D models. The 4D CNN model improves Alzheimers disease diagnosis for rs-fMRI data, enabling earlier detection and better interventions. Future research could explore task-based fMRI applications and regression tasks, enhancing understanding of cognitive performance and disease progression.
CineMA: A Foundation Model for Cine Cardiac MRI
Authors: Yunguan Fu, Weixi Yi, Charlotte Manisty, Anish N Bhuva, Thomas A Treibel, James C Moon, Matthew J Clarkson, Rhodri Huw Davies, Yipeng Hu
Date: 2025-05-31 00:00:00
Tasks: Myocardium Segmentation

Cardiac magnetic resonance (CMR) is a key investigation in clinical cardiovascular medicine and has been used extensively in population research. However, extracting clinically important measurements such as ejection fraction for diagnosing cardiovascular diseases remains time-consuming and subjective. We developed CineMA, a foundation AI model automating these tasks with limited labels. CineMA is a self-supervised autoencoder model trained on 74,916 cine CMR studies to reconstruct images from masked inputs. After fine-tuning, it was evaluated across eight datasets on 23 tasks from four categories: ventricle and myocardium segmentation, left and right ventricle ejection fraction calculation, disease detection and classification, and landmark localisation. CineMA is the first foundation model for cine CMR to match or outperform convolutional neural networks (CNNs). CineMA demonstrated greater label efficiency than CNNs, achieving comparable or better performance with fewer annotations. This reduces the burden of clinician labelling and supports replacing task-specific training with fine-tuning foundation models in future cardiac imaging applications. Models and code for pre-training and fine-tuning are available at https://github.com/mathpluscode/CineMA, democratising access to high-performance models that otherwise require substantial computational resources, promoting reproducibility and accelerating clinical translation.
MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models
Authors: Xudong Ma, Nantheera Anantrasirichai, Stefanos Bolomytis, Alin Achim
Date: 2025-05-31 00:00:00
Tasks: 3D Reconstruction, Anatomy

The diagnosis of prostate cancer increasingly depends on multimodal imaging, particularly magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS). However, accurate registration between these modalities remains a fundamental challenge due to the differences in dimensionality and anatomical representations. In this work, we present a novel framework that addresses these challenges through a two-stage process: TRUS 3D reconstruction followed by cross-modal registration. Unlike existing TRUS 3D reconstruction methods that rely heavily on external probe tracking information, we propose a totally probe-location-independent approach that leverages the natural correlation between sagittal and transverse TRUS views. With the help of our clustering-based feature matching method, we enable the spatial localization of 2D frames without any additional probe tracking information. For the registration stage, we introduce an unsupervised diffusion-based framework guided by modality translation. Unlike existing methods that translate one modality into another, we map both MR and US into a pseudo intermediate modality. This design enables us to customize it to retain only registration-critical features, greatly easing registration. To further enhance anatomical alignment, we incorporate an anatomy-aware registration strategy that prioritizes internal structural coherence while adaptively reducing the influence of boundary inconsistencies. Extensive validation demonstrates that our approach outperforms state-of-the-art methods by achieving superior registration accuracy with physically realistic deformations in a completely unsupervised fashion.
Image Restoration Learning via Noisy Supervision in the Fourier Domain
Authors: Haosen Liu, Jiahao Liu, Shan Tan, Edmund Y. Lam
Date: 2025-05-31 00:00:00
Tasks: Image Restoration

Noisy supervision refers to supervising image restoration learning with noisy targets. It can alleviate the data collection burden and enhance the practical applicability of deep learning techniques. However, existing methods suffer from two key drawbacks. Firstly, they are ineffective in handling spatially correlated noise commonly observed in practical applications such as low-light imaging and remote sensing. Secondly, they rely on pixel-wise loss functions that only provide limited supervision information. This work addresses these challenges by leveraging the Fourier domain. We highlight that the Fourier coefficients of spatially correlated noise exhibit sparsity and independence, making them easier to handle. Additionally, Fourier coefficients contain global information, enabling more significant supervision. Motivated by these insights, we propose to establish noisy supervision in the Fourier domain. We first prove that Fourier coefficients of a wide range of noise converge in distribution to the Gaussian distribution. Exploiting this statistical property, we establish the equivalence between using noisy targets and clean targets in the Fourier domain. This leads to a unified learning framework applicable to various image restoration tasks, diverse network architectures, and different noise models. Extensive experiments validate the outstanding performance of this framework in terms of both quantitative indices and perceptual quality.
Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
Authors: Basudha Pal, Rama Chellappa, Muhammad Umair
Date: 2025-05-31 00:00:00
Tasks: Attribute

While echocardiography and MRI are clinical standards for evaluating cardiac structure, their use is limited by cost and accessibility.We introduce a direct classification framework that predicts severe left ventricular hypertrophy from chest X-rays, without relying on anatomical measurements or demographic inputs. Our approach achieves high AUROC and AUPRC, and employs Mutual Information Neural Estimation to quantify feature expressivity. This reveals clinically meaningful attribute encoding and supports transparent model interpretation.
Your Demands Deserve More Bits: Referring Semantic Image Compression at Ultra-low Bitrate
Authors: Chenhao Wu, Qingbo Wu, Haoran Wei, Shuai Chen, Mingzhou He, King Ngi Ngan, Fanman Meng, Hongliang Li
Date: 2025-05-31 00:00:00
Tasks: Image Compression

With the help of powerful generative models, Semantic Image Compression (SIC) has achieved impressive performance at ultra-low bitrate. However, due to coarse-grained visual-semantic alignment and inherent randomness, the reliability of SIC is seriously concerned for reconstructing completely different object instances, even they are semantically consistent with original images. To tackle this issue, we propose a novel Referring Semantic Image Compression (RSIC) framework to improve the fidelity of user-specified content while retaining extreme compression ratios. Specifically, RSIC consists of three modules: Global Description Encoding (GDE), Referring Guidance Encoding (RGE), and Guided Generative Decoding (GGD). GDE and RGE encode global semantic information and local features, respectively, while GGD handles the non-uniformly guided generative process based on the encoded information. In this way, our RSIC achieves flexible customized compression according to user demands, which better balance the local fidelity, global realism, semantic alignment, and bit overhead. Extensive experiments on three datasets verify the compression efficiency and flexibility of the proposed method.
UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
Authors: Raghav Mehta, Karthik Gopinath, Ben Glocker, Juan Eugenio Iglesias
Date: 2025-05-31 00:00:00
Tasks: Surface Reconstruction, Uncertainty Quantification

We propose UNSURF, a novel uncertainty measure for cortical surface reconstruction of clinical brain MRI scans of any orientation, resolution, and contrast. It relies on the discrepancy between predicted voxel-wise signed distance functions (SDFs) and the actual SDFs of the fitted surfaces. Our experiments on real clinical scans show that traditional uncertainty measures, such as voxel-wise Monte Carlo variance, are not suitable for modeling the uncertainty of surface placement. Our results demonstrate that UNSURF estimates correlate well with the ground truth errors and: \textit{(i)}~enable effective automated quality control of surface reconstructions at the subject-, parcel-, mesh node-level; and \textit{(ii)}~improve performance on a downstream Alzheimer's disease classification task.
A European Multi-Center Breast Cancer MRI Dataset
Authors: Gustav Müller-Franzes, Lorena Escudero Sánchez, Nicholas Payne, Alexandra Athanasiou, Michael Kalogeropoulos, Aitor Lopez, Alfredo Miguel Soro Busto, Julia Camps Herrero, Nika Rasoolzadeh, Tianyu Zhang, Ritse Mann, Debora Jutz, Maike Bode, Christiane Kuhl, Wouter Veldhuis, Oliver Lester Saldanha, Jiefu Zhu, Jakob Nikolas Kather, Daniel Truhn, Fiona J. Gilbert
Date: 2025-05-31 00:00:00

Detecting breast cancer early is of the utmost importance to effectively treat the millions of women afflicted by breast cancer worldwide every year. Although mammography is the primary imaging modality for screening breast cancer, there is an increasing interest in adding magnetic resonance imaging (MRI) to screening programmes, particularly for women at high risk. Recent guidelines by the European Society of Breast Imaging (EUSOBI) recommended breast MRI as a supplemental screening tool for women with dense breast tissue. However, acquiring and reading MRI scans requires significantly more time from expert radiologists. This highlights the need to develop new automated methods to detect cancer accurately using MRI and Artificial Intelligence (AI), which have the potential to support radiologists in breast MRI interpretation and classification and help detect cancer earlier. For this reason, the ODELIA consortium has made this multi-centre dataset publicly available to assist in developing AI tools for the detection of breast cancer on MRI.
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding
Authors: Tuan-Luc Huynh, Thanh-Danh Le, Tam V. Nguyen, Trung-Nghia Le, Minh-Triet Tran
Date: 2025-05-31 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

In this paper, we address the crucial task of brain tumor segmentation in medical imaging and propose innovative approaches to enhance its performance. The current state-of-the-art nnU-Net has shown promising results but suffers from extensive training requirements and underutilization of pre-trained weights. To overcome these limitations, we integrate Axial-Coronal-Sagittal convolutions and pre-trained weights from ImageNet into the nnU-Net framework, resulting in reduced training epochs, reduced trainable parameters, and improved efficiency. Two strategies for transferring 2D pre-trained weights to the 3D domain are presented, ensuring the preservation of learned relationships and feature representations critical for effective information propagation. Furthermore, we explore a joint classification and segmentation model that leverages pre-trained encoders from a brain glioma grade classification proxy task, leading to enhanced segmentation performance, especially for challenging tumor labels. Experimental results demonstrate that our proposed methods in the fast training settings achieve comparable or even outperform the ensemble of cross-validation models, a common practice in the brain tumor segmentation literature.
Adaptive Voxelization for Transform coding of 3D Gaussian splatting data
Authors: Chenjunjie Wang, Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega, Cheng Chang
Date: 2025-05-30 00:00:00
Tasks: 3DGS

We present a novel compression framework for 3D Gaussian splatting (3DGS) data that leverages transform coding tools originally developed for point clouds. Contrary to existing 3DGS compression methods, our approach can produce compressed 3DGS models at multiple bitrates in a computationally efficient way. Point cloud voxelization is a discretization technique that point cloud codecs use to improve coding efficiency while enabling the use of fast transform coding algorithms. We propose an adaptive voxelization algorithm tailored to 3DGS data, to avoid the inefficiencies introduced by uniform voxelization used in point cloud codecs. We ensure the positions of larger volume Gaussians are represented at high resolution, as these significantly impact rendering quality. Meanwhile, a low-resolution representation is used for dense regions with smaller Gaussians, which have a relatively lower impact on rendering quality. This adaptive voxelization approach significantly reduces the number of Gaussians and the bitrate required to encode the 3DGS data. After voxelization, many Gaussians are moved or eliminated. Thus, we propose to fine-tune/recolor the remaining 3DGS attributes with an initialization that can reduce the amount of retraining required. Experimental results on pre-trained datasets show that our proposed compression framework outperforms existing methods.
Lightweight Convolutional Neural Networks for Retinal Disease Classification
Authors: Duaa Kareem Qasim, Sabah Abdulazeez Jebur, Lafta Raheem Ali, Abdul Jalil M. Khalaf, Abir Jaafar Hussain
Date: 2025-05-30 00:00:00
Tasks: Classification, Data Augmentation, Transfer Learning

Retinal diseases such as Diabetic Retinopathy (DR) and Macular Hole (MH) significantly impact vision and affect millions worldwide. Early detection is crucial, as DR, a complication of diabetes, damages retinal blood vessels, potentially leading to blindness, while MH disrupts central vision, affecting tasks like reading and facial recognition. This paper employed two lightweight and efficient Convolution Neural Network architectures, MobileNet and NASNetMobile, for the classification of Normal, DR, and MH retinal images. The models were trained on the RFMiD dataset, consisting of 3,200 fundus images, after undergoing preprocessing steps such as resizing, normalization, and augmentation. To address data scarcity, this study leveraged transfer learning and data augmentation techniques, enhancing model generalization and performance. The experimental results demonstrate that MobileNetV2 achieved the highest accuracy of 90.8%, outperforming NASNetMobile, which achieved 89.5% accuracy. These findings highlight the effectiveness of CNNs in retinal disease classification, providing a foundation for AI-assisted ophthalmic diagnosis and early intervention.
DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset
Authors: Liangrui Pan, Xingchen Li, Zhongyi Chen, Ling Chu, Shaoliang Peng
Date: 2025-05-30 00:00:00
Tasks: Multiple Instance Learning, whole slide images

Pathologists comprehensive evaluation of donor liver biopsies provides crucial information for accepting or discarding potential grafts. However, rapidly and accurately obtaining these assessments intraoperatively poses a significant challenge for pathologists. Features in donor liver biopsies, such as portal tract fibrosis, total steatosis, macrovesicular steatosis, and hepatocellular ballooning are correlated with transplant outcomes, yet quantifying these indicators suffers from substantial inter- and intra-observer variability. To address this, we introduce DLiPath, the first benchmark for comprehensive donor liver assessment based on a histopathology image dataset. We collected and publicly released 636 whole slide images from 304 donor liver patients at the Department of Pathology, the Third Xiangya Hospital, with expert annotations for key pathological features (including cholestasis, portal tract fibrosis, portal inflammation, total steatosis, macrovesicular steatosis, and hepatocellular ballooning). We selected nine state-of-the-art multiple-instance learning (MIL) models based on the DLiPath dataset as baselines for extensive comparative analysis. The experimental results demonstrate that several MIL models achieve high accuracy across donor liver assessment indicators on DLiPath, charting a clear course for future automated and intelligent donor liver assessment research. Data and code are available at https://github.com/panliangrui/ACM_MM_2025.
Deep learning-derived arterial input function
Authors: Junyu Chen, Zirui Jiang, Jennifer M. Coughlin, Martin G. Pomper, Yong Du
Date: 2025-05-30 00:00:00
Tasks: Deep Learning

Dynamic positron emission tomography (PET) imaging combined with radiotracer kinetic modeling is a powerful technique for visualizing biological processes in the brain, offering valuable insights into brain functions and neurological disorders such as Alzheimer's and Parkinson's diseases. Accurate kinetic modeling relies heavily on the use of a metabolite-corrected arterial input function (AIF), which typically requires invasive and labor-intensive arterial blood sampling. While alternative non-invasive approaches have been proposed, they often compromise accuracy or still necessitate at least one invasive blood sampling. In this study, we present the deep learning-derived arterial input function (DLIF), a deep learning framework capable of estimating a metabolite-corrected AIF directly from dynamic PET image sequences without any blood sampling. We validated DLIF using existing dynamic PET patient data. We compared DLIF and resulting parametric maps against ground truth measurements. Our evaluation shows that DLIF achieves accurate and robust AIF estimation. By leveraging deep learning's ability to capture complex temporal dynamics and incorporating prior knowledge of typical AIF shapes through basis functions, DLIF provides a rapid, accurate, and entirely non-invasive alternative to traditional AIF measurement methods.
Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning
Authors: Wangting Zhou, Jiangshan He, Tong Cai, Lin Wang, Zhen Yuan, Xunbin Wei, Xueli Chen
Date: 2025-05-29 00:00:00
Tasks: Multi Focus Image Fusion

Photoacoustic microscopy holds the potential to measure biomarkers' structural and functional status without labels, which significantly aids in comprehending pathophysiological conditions in biomedical research. However, conventional optical-resolution photoacoustic microscopy (OR-PAM) is hindered by a limited depth-of-field (DoF) due to the narrow depth range focused on a Gaussian beam. Consequently, it fails to resolve sufficient details in the depth direction. Herein, we propose a decision-level constrained end-to-end multi-focus image fusion (Dc-EEMF) to push DoF limit of PAM. The DC-EEMF method is a lightweight siamese network that incorporates an artifact-resistant channel-wise spatial frequency as its feature fusion rule. The meticulously crafted U-Net-based perceptual loss function for decision-level focus properties in end-to-end fusion seamlessly integrates the complementary advantages of spatial domain and transform domain methods within Dc-EEMF. This approach can be trained end-to-end without necessitating post-processing procedures. Experimental results and numerical analyses collectively demonstrate our method's robust performance, achieving an impressive fusion result for PAM images without a substantial sacrifice in lateral resolution. The utilization of Dc-EEMF-powered PAM has the potential to serve as a practical tool in preclinical and clinical studies requiring extended DoF for various applications.
LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning
Authors: Md. Zihad Bin Jahangir, Muhammad Ashad Kabir, Sumaiya Akter, Israt Jahan, Minh Chau
Date: 2025-05-29 00:00:00
Tasks: Computational Efficiency, Diagnostic

Automated radiology report generation holds significant potential to reduce radiologists' workload and enhance diagnostic accuracy. However, generating precise and clinically meaningful reports from chest radiographs remains challenging due to the complexity of medical language and the need for contextual understanding. Existing models often struggle with maintaining both accuracy and contextual relevance. In this paper, we present LLaMA-XR, a novel framework that integrates LLaMA 3.1 with DenseNet-121-based image embeddings and Quantized Low-Rank Adaptation (QLoRA) fine-tuning. LLaMA-XR achieves improved coherence and clinical accuracy while maintaining computational efficiency. This efficiency is driven by an optimization strategy that enhances parameter utilization and reduces memory overhead, enabling faster report generation with lower computational resource demands. Extensive experiments conducted on the IU X-ray benchmark dataset demonstrate that LLaMA-XR outperforms a range of state-of-the-art methods. Our model achieves a ROUGE-L score of 0.433 and a METEOR score of 0.336, establishing new performance benchmarks in the domain. These results underscore LLaMA-XR's potential as an effective and efficient AI system for automated radiology reporting, offering enhanced clinical utility and reliability.