pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomas
Authors: Kiranmayee Janardhan, Christy Bobby T
Date: 2025-02-27 00:00:00
Tasks: Computational Efficiency, MRI segmentation, Segmentation

Brain tumor diagnosis is a challenging task for clinicians in the modern world. Among the major reasons for cancer-related death is the brain tumor. Gliomas, a category of central nervous system (CNS) tumors, encompass diverse subregions. For accurate diagnosis of brain tumors, precise segmentation of brain images and quantitative analysis are required. A fully automatic approach to glioma segmentation is required because the manual segmentation process is laborious, prone to mistakes, as well as time-consuming. Modern techniques for segmenting gliomas are based on fully convolutional neural networks (FCNs), which can either use two-dimensional (2D) or three-dimensional (3D) convolutions. Nevertheless, 3D convolutions suffer from computational costs and memory demand, while 2D convolutions cannot fully utilize the spatial insights of volumetric clinical imaging data. To obtain an optimal solution, it is vital to balance the computational efficiency of 2D convolutions along with the spatial accuracy of 3D convolutions. This balance can potentially be realized by developing an advanced model to overcome these challenges. The 2D and 3D models implemented here are based on UNET architecture, Inception, and ResNet models. The research work has been implemented on the BraTS 2018, 2019, and 2020 datasets. The best performer of all the models' evaluations metrics for proposed methodologies offer superior potential in terms of the effective segmentation of gliomas. The ResNet model has resulted in 98.91% accuracy for 3D segmentation and 99.77 for 2D segmentations. The dice scores for 2D and 3D segmentations are 0.8312 and 0.9888, respectively. This model can be applied to various other medical applications with fine-tuning, thereby aiding clinicians in brain tumor analysis and improving the diagnosis process effectively.
A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging
Authors: Junji Lin, Yi Zhang, Yunyue Pan, Yuli Chen, Chengchang Pan, Honggang Qi
Date: 2025-02-27 00:00:00
Tasks: image-classification, Image Classification, Lung Nodule Detection, Medical Image Analysis, Multi-Task Learning

Detection and classification of pulmonary nodules is a challenge in medical image analysis due to the variety of shapes and sizes of nodules and their high concealment. Despite the success of traditional deep learning methods in image classification, deep networks still struggle to perfectly capture subtle changes in lung nodule detection. Therefore, we propose a residual multi-task network (Res-MTNet) model, which combines multi-task learning and residual learning, and improves feature representation ability by sharing feature extraction layer and introducing residual connections. Multi-task learning enables the model to handle multiple tasks simultaneously, while the residual module solves the problem of disappearing gradients, ensuring stable training of deeper networks and facilitating information sharing between tasks. Res-MTNet enhances the robustness and accuracy of the model, providing a more reliable lung nodule analysis tool for clinical medicine and telemedicine.
Dual-branch Graph Feature Learning for NLOS Imaging
Authors: Xiongfei Su, Tianyi Zhu, Lina Liu, Zheng Chen, Yulun Zhang, Siyuan Li, Juntian Ye, Feihu Xu, Xin Yuan
Date: 2025-02-27 00:00:00

The domain of non-line-of-sight (NLOS) imaging is advancing rapidly, offering the capability to reveal occluded scenes that are not directly visible. However, contemporary NLOS systems face several significant challenges: (1) The computational and storage requirements are profound due to the inherent three-dimensional grid data structure, which restricts practical application. (2) The simultaneous reconstruction of albedo and depth information requires a delicate balance using hyperparameters in the loss function, rendering the concurrent reconstruction of texture and depth information difficult. This paper introduces the innovative methodology, \xnet, which integrates an albedo-focused reconstruction branch dedicated to albedo information recovery and a depth-focused reconstruction branch that extracts geometrical structure, to overcome these obstacles. The dual-branch framework segregates content delivery to the respective reconstructions, thereby enhancing the quality of the retrieved data. To our knowledge, we are the first to employ the GNN as a fundamental component to transform dense NLOS grid data into sparse structural features for efficient reconstruction. Comprehensive experiments demonstrate that our method attains the highest level of performance among existing methods across synthetic and real data. https://github.com/Nicholassu/DG-NLOS.
GONet: A Generalizable Deep Learning Model for Glaucoma Detection
Authors: Or Abramovich, Hadas Pizem, Jonathan Fhima, Eran Berkowitz, Ben Gofrit, Meishar Meisel, Meital Baskin, Jan Van Eijgen, Ingeborg Stalmans, Eytan Z. Blumenthal, Joachim A. Behar
Date: 2025-02-26 00:00:00
Tasks: Deep Learning, Diagnostic

Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated. The traditional diagnostic approach for GON involves a set of ophthalmic examinations, which are time-consuming and require a visit to an ophthalmologist. Recent deep learning models for automating GON detection from digital fundus images (DFI) have shown promise but often suffer from limited generalizability across different ethnicities, disease groups and examination settings. To address these limitations, we introduce GONet, a robust deep learning model developed using seven independent datasets, including over 119,000 DFIs with gold-standard annotations and from patients of diverse geographic backgrounds. GONet consists of a DINOv2 pre-trained self-supervised vision transformers fine-tuned using a multisource domain strategy. GONet demonstrated high out-of-distribution generalizability, with an AUC of 0.85-0.99 in target domains. GONet performance was similar or superior to state-of-the-art works and was significantly superior to the cup-to-disc ratio, by up to 21.6%. GONet is available at [URL provided on publication]. We also contribute a new dataset consisting of 768 DFI with GON labels as open access.
Multi-modal Contrastive Learning for Tumor-specific Missing Modality Synthesis
Authors: Minjoo Lim, Bogyeong Kang, Tae-Eui Kam
Date: 2025-02-26 00:00:00
Tasks: Anatomy, Contrastive Learning, Image Generation, Segmentation

Multi-modal magnetic resonance imaging (MRI) is essential for providing complementary information about brain anatomy and pathology, leading to more accurate diagnoses. However, obtaining high-quality multi-modal MRI in a clinical setting is difficult due to factors such as time constraints, high costs, and patient movement artifacts. To overcome this difficulty, there is increasing interest in developing generative models that can synthesize missing target modality images from the available source ones. Therefore, our team, PLAVE, design a generative model for missing MRI that integrates multi-modal contrastive learning with a focus on critical tumor regions. Specifically, we integrate multi-modal contrastive learning, tailored for multiple source modalities, and enhance its effectiveness by selecting features based on entropy during the contrastive learning process. Additionally, our network not only generates the missing target modality images but also predicts segmentation outputs, simultaneously. This approach improves the generator's capability to precisely generate tumor regions, ultimately improving performance in downstream segmentation tasks. By leveraging a combination of contrastive, segmentation, and additional self-representation losses, our model effectively reflects target-specific information and generate high-quality target images. Consequently, our results in the Brain MR Image Synthesis challenge demonstrate that the proposed model excelled in generating the missing modality.
Deep Learning-Based Transfer Learning for Classification of Cassava Disease
Authors: Ademir G. Costa Junior, Fábio S. da Silva, Ricardo Rios
Date: 2025-02-26 00:00:00
Tasks: Transfer Learning

This paper presents a performance comparison among four Convolutional Neural Network architectures (EfficientNet-B3, InceptionV3, ResNet50, and VGG16) for classifying cassava disease images. The images were sourced from an imbalanced dataset from a competition. Appropriate metrics were employed to address class imbalance. The results indicate that EfficientNet-B3 achieved on this task accuracy of 87.7%, precision of 87.8%, revocation of 87.8% and F1-Score of 87.7%. These findings suggest that EfficientNet-B3 could be a valuable tool to support Digital Agriculture.
Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification
Authors: Anyimadu Daniel Tweneboah, Suleiman Taofik Ahmed, Hossain Mohammad Imran
Date: 2025-02-26 00:00:00
Tasks: Image Registration, Lesion Classification, Medical Image Analysis, Skin Lesion Classification

Medical imaging spans diverse tasks and modalities which play a pivotal role in disease diagnosis, treatment planning, and monitoring. This study presents a novel exploration, being the first to systematically evaluate segmentation, registration, and classification tasks across multiple imaging modalities. Integrating both classical and deep learning (DL) approaches in addressing brain MRI tissue segmentation, lung CT image registration, and skin lesion classification from dermoscopic images, we demonstrate the complementary strengths of these methodologies in diverse applications. For brain tissue segmentation, 3D DL models outperformed 2D and patch-based models, specifically nnU-Net achieving Dice of 0.9397, with 3D U-Net models on ResNet34 backbone, offering competitive results with Dice 0.8946. Multi-Atlas methods provided robust alternatives for cases where DL methods are not feasible, achieving average Dice of 0.7267. In lung CT registration, classical Elastix-based methods outperformed DL models, achieving a minimum Target Registration Error (TRE) of 6.68 mm, highlighting the effectiveness of parameter tuning. HighResNet performed best among DL models with a TRE of 7.40 mm. For skin lesion classification, ensembles of DL models like InceptionResNetV2 and ResNet50 excelled, achieving up to 90.44%, and 93.62% accuracies for binary and multiclass classification respectively. Also, adopting One-vs-All method, DL attained accuracies of 94.64% (mel vs. others), 95.35% (bcc vs. others), and 96.93% (scc vs. others), while ML models specifically Multi-Layer Perceptron (MLP) on handcrafted features offered interpretable alternatives with 85.04% accuracy using SMOTE for class imbalance correction on the multi-class task and 83.27% on the binary-class task. Links to source code are available on request.
Arbitrary Volumetric Refocusing of Dense and Sparse Light Fields
Authors: Tharindu Samarakoon, Kalana Abeywardena, Chamira U. S. Edussooriya
Date: 2025-02-26 00:00:00

A four-dimensional light field (LF) captures both textural and geometrical information of a scene in contrast to a two-dimensional image that captures only the textural information of a scene. Post-capture refocusing is an exciting application of LFs enabled by the geometric information captured. Previously proposed LF refocusing methods are mostly limited to the refocusing of single planar or volumetric region of a scene corresponding to a depth range and cannot simultaneously generate in-focus and out-of-focus regions having the same depth range. In this paper, we propose an end-to-end pipeline to simultaneously refocus multiple arbitrary planar or volumetric regions of a dense or a sparse LF. We employ pixel-dependent shifts with the typical shift-and-sum method to refocus an LF. The pixel-dependent shifts enables to refocus each pixel of an LF independently. For sparse LFs, the shift-and-sum method introduces ghosting artifacts due to the spatial undersampling. We employ a deep learning model based on U-Net architecture to almost completely eliminate the ghosting artifacts. The experimental results obtained with several LF datasets confirm the effectiveness of the proposed method. In particular, sparse LFs refocused with the proposed method archive structural similarity index higher than 0.9 despite having only 20% of data compared to dense LFs.
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems
Authors: Bernardin Tamo Amougou, Marcelo Pereyra, Barbara Pascal
Date: 2025-02-26 00:00:00
Tasks: Conformal Prediction, Deblurring, Denoising, Image Denoising, Image Restoration, Prediction, Uncertainty Quantification

Image restoration problems are often ill-posed, leading to significant uncertainty in reconstructed images. Accurately quantifying this uncertainty is essential for the reliable interpretation of reconstructed images. However, image restoration methods often lack uncertainty quantification capabilities. Conformal prediction offers a rigorous framework to augment image restoration methods with accurate uncertainty quantification estimates, but it typically requires abundant ground truth data for calibration. This paper presents a self-supervised conformal prediction method for Poisson imaging problems which leverages Poisson Unbiased Risk Estimator to eliminate the need for ground truth data. The resulting self-calibrating conformal prediction approach is applicable to any Poisson linear imaging problem that is ill-conditioned, and is particularly effective when combined with modern self-supervised image restoration techniques trained directly on measurement data. The proposed method is demonstrated through numerical experiments on image denoising and deblurring; its performance are comparable to supervised conformal prediction methods relying on ground truth data.
Multi-level Attention-guided Graph Neural Network for Image Restoration
Authors: Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang
Date: 2025-02-26 00:00:00
Tasks: Graph Neural Network, Image Restoration

In recent years, deep learning has achieved remarkable success in the field of image restoration. However, most convolutional neural network-based methods typically focus on a single scale, neglecting the incorporation of multi-scale information. In image restoration tasks, local features of an image are often insufficient, necessitating the integration of global features to complement them. Although recent neural network algorithms have made significant strides in feature extraction, many models do not explicitly model global features or consider the relationship between global and local features. This paper proposes multi-level attention-guided graph neural network. The proposed network explicitly constructs element block graphs and element graphs within feature maps using multi-attention mechanisms to extract both local structural features and global representation information of the image. Since the network struggles to effectively extract global information during image degradation, the structural information of local feature blocks can be used to correct and supplement the global information. Similarly, when element block information in the feature map is missing, it can be refined using global element representation information. The graph within the network learns real-time dynamic connections through the multi-attention mechanism, and information is propagated and aggregated via graph convolution algorithms. By combining local element block information and global element representation information from the feature map, the algorithm can more effectively restore missing information in the image. Experimental results on several classic image restoration tasks demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance.
From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review
Authors: Behnaz Elhaminia, Abdullah Alsalemi, Esha Nasir, Mostafa Jahanifar, Ruqayya Awan, Lawrence S. Young, Nasir M. Rajpoot, Fayyaz Minhas, Shan E Ahmed Raza
Date: 2025-02-26 00:00:00
Tasks: Image Registration

Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighbouring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artefacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasising their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.
Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention
Authors: Jiebin Yan, Ziwen Tan, Yuming Fang, Jiale Rao, Yifan Zuo
Date: 2025-02-26 00:00:00
Tasks: Image Quality Assessment

Omnidirectional image, also called 360-degree image, is able to capture the entire 360-degree scene, thereby providing more realistic immersive feelings for users than general 2D image and stereoscopic image. Meanwhile, this feature brings great challenges to measuring the perceptual quality of omnidirectional images, which is closely related to users' quality of experience, especially when the omnidirectional images suffer from non-uniform distortion. In this paper, we propose a novel and effective blind omnidirectional image quality assessment (BOIQA) model with multi-axis attention (Max360IQ), which can proficiently measure not only the quality of uniformly distorted omnidirectional images but also the quality of non-uniformly distorted omnidirectional images. Specifically, the proposed Max360IQ is mainly composed of a backbone with stacked multi-axis attention modules for capturing both global and local spatial interactions of extracted viewports, a multi-scale feature integration (MSFI) module to fuse multi-scale features and a quality regression module with deep semantic guidance for predicting the quality of omnidirectional images. Experimental results demonstrate that the proposed Max360IQ outperforms the state-of-the-art Assessor360 by 3.6\% in terms of SRCC on the JUFE database with non-uniform distortion, and gains improvement of 0.4\% and 0.8\% in terms of SRCC on the OIQA and CVIQ databases, respectively. The source code is available at https://github.com/WenJuing/Max360IQ.
PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics
Authors: Pu Wang, Huaizhi Ma, Zhihua Zhang, Zhuoran Zheng
Date: 2025-02-26 00:00:00
Tasks: Segmentation

Accurate polyp segmentation remains challenging due to irregular lesion morphologies, ambiguous boundaries, and heterogeneous imaging conditions. While U-Net variants excel at local feature fusion, they often lack explicit mechanisms to model the dynamic evolution of segmentation confidence under uncertainty. Inspired by the interpretable nature of flow-based models, we present \textbf{PolypFLow}, a flow-matching enhanced architecture that injects physics-inspired optimization dynamics into segmentation refinement. Unlike conventional cascaded networks, our framework solves an ordinary differential equation (ODE) to progressively align coarse initial predictions with ground truth masks through learned velocity fields. This trajectory-based refinement offers two key advantages: 1) Interpretable Optimization: Intermediate flow steps visualize how the model corrects under-segmented regions and sharpens boundaries at each ODE-solver iteration, demystifying the ``black-box" refinement process; 2) Boundary-Aware Robustness: The flow dynamics explicitly model gradient directions along polyp edges, enhancing resilience to low-contrast regions and motion artifacts. Numerous experimental results show that PolypFLow achieves a state-of-the-art while maintaining consistent performance in different lighting scenarios.
InternVQA: Advancing Compressed Video Quality Assessment with Distilling Large Foundation Model
Authors: Fengbin Guan, Zihao Yu, Yiting Lu, Xin Li, Zhibo Chen
Date: 2025-02-26 00:00:00
Tasks: Video Quality Assessment, Video Understanding

Video quality assessment tasks rely heavily on the rich features required for video understanding, such as semantic information, texture, and temporal motion. The existing video foundational model, InternVideo2, has demonstrated strong potential in video understanding tasks due to its large parameter size and large-scale multimodal data pertaining. Building on this, we explored the transferability of InternVideo2 to video quality assessment under compression scenarios. To design a lightweight model suitable for this task, we proposed a distillation method to equip the smaller model with rich compression quality priors. Additionally, we examined the performance of different backbones during the distillation process. The results showed that, compared to other methods, our lightweight model distilled from InternVideo2 achieved excellent performance in compression video quality assessment.
ILACS-LGOT: A Multi-Layer Contrast Enhancement Approach for Palm-Vein Images
Authors: Kaveen Perera, Fouad Khelifi, Ammar Belatreche
Date: 2025-02-26 00:00:00
Tasks: Image Enhancement

This article presents an extended author's version based on our previous work, where we introduced the Multiple Overlapping Tiles (MOT) method for palm vein image enhancement. To better reflect the specific operations involved, we rename MOT to ILACS-LGOT (Intensity-Limited Adaptive Contrast Stretching with Layered Gaussian-weighted Overlapping Tiles). This revised terminology more accurately represents the method's approach to contrast enhancement and blocky effect mitigation. Additionally, this article provides a more detailed analysis, including expanded evaluations, graphical representations, and sample-based comparisons, demonstrating the effectiveness of ILACS-LGOT over existing methods.