pwc-archive/papers-with-abstracts huggingface dataset:
PG-DPIR: An efficient plug-and-play method for high-count Poisson-Gaussian inverse problems
Authors: Maud Biquard, Marie Chabert, Florence Genin, Christophe Latry, Thomas Oberlin
Date: 2025-04-14 00:00:00
Tasks: Image Restoration, Super-Resolution

Poisson-Gaussian noise describes the noise of various imaging systems thus the need of efficient algorithms for Poisson-Gaussian image restoration. Deep learning methods offer state-of-the-art performance but often require sensor-specific training when used in a supervised setting. A promising alternative is given by plug-and-play (PnP) methods, which consist in learning only a regularization through a denoiser, allowing to restore images from several sources with the same network. This paper introduces PG-DPIR, an efficient PnP method for high-count Poisson-Gaussian inverse problems, adapted from DPIR. While DPIR is designed for white Gaussian noise, a naive adaptation to Poisson-Gaussian noise leads to prohibitively slow algorithms due to the absence of a closed-form proximal operator. To address this, we adapt DPIR for the specificities of Poisson-Gaussian noise and propose in particular an efficient initialization of the gradient descent required for the proximal step that accelerates convergence by several orders of magnitude. Experiments are conducted on satellite image restoration and super-resolution problems. High-resolution realistic Pleiades images are simulated for the experiments, which demonstrate that PG-DPIR achieves state-of-the-art performance with improved efficiency, which seems promising for on-ground satellite processing chains.
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
Authors: Ziyao Shang, Misha Kaandorp, Kelly Payette, Marina Fernandez Garcia, Roxane Licandro, Georg Langs, Jordina Aviles Verdera, Jana Hutter, Bjoern Menze, Gregor Kasprian, Meritxell Bach Cuadra, Andras Jakab
Date: 2025-04-14 00:00:00
Tasks: Data Augmentation, MRI segmentation

Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis of the developing human brain, with Deep learning providing an automated alternative for this otherwise tedious manual process. However, segmentation performances of Convolutional Neural Networks often suffer from domain shift, where the network fails when applied to subjects that deviate from the distribution with which it is trained on. In this work, we aim to train networks capable of automatically segmenting fetal brain MRIs with a wide range of domain shifts pertaining to differences in subject physiology and acquisition environments, in particular shape-based differences commonly observed in pathological cases. We introduce a novel data-driven train-time sampling strategy that seeks to fully exploit the diversity of a given training dataset to enhance the domain generalizability of the trained networks. We adapted our sampler, together with other existing data augmentation techniques, to the SynthSeg framework, a generator that utilizes domain randomization to generate diverse training data, and ran thorough experimentations and ablation studies on a wide range of training/testing data to test the validity of the approaches. Our networks achieved notable improvements in the segmentation quality on testing subjects with intense anatomical abnormalities (p < 1e-4), though at the cost of a slighter decrease in performance in cases with fewer abnormalities. Our work also lays the foundation for future works on creating and adapting data-driven sampling strategies for other training pipelines.
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction
Authors: Yucheng Lu, Shunxin Wang, Dovile Juodelyte, Veronika Cheplygina
Date: 2025-04-14 00:00:00
Tasks: Exposure Correction, Image Enhancement, Image Harmonization, Medical Image Analysis, Transfer Learning

In this paper, we explore how conventional image enhancement can improve model robustness in medical image analysis. By applying commonly used normalization methods to images from various vendors and studying their influence on model generalization in transfer learning, we show that the nonlinear characteristics of domain-specific image dynamics cannot be addressed by simple linear transforms. To tackle this issue, we reformulate the image harmonization task as an exposure correction problem and propose a method termed Global Deep Curve Estimation (GDCE) to reduce domain-specific exposure mismatch. GDCE performs enhancement via a pre-defined polynomial function and is trained with the help of a ``domain discriminator'', aiming to improve model transparency in downstream tasks compared to existing black-box methods.
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration
Authors: Uyen Phan, Ozer Can Devecioglu, Serkan Kiranyaz, Moncef Gabbouj
Date: 2025-04-14 00:00:00
Tasks: Diabetic Retinopathy Detection, Image Restoration, Transfer Learning

Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of poor quality fundus images caused by factors such as inadequate illumination, noise, blurring and other motion artifacts yields a significant challenge for accurate DR screening. In this study, we propose progressive transfer learning for multi pass restoration to iteratively enhance the quality of degraded fundus images, ensuring more reliable DR screening. Unlike previous methods that often focus on a single pass restoration, multi pass restoration via PTL can achieve a superior blind restoration performance that can even improve most of the good quality fundus images in the dataset. Initially, a Cycle GAN model is trained to restore low quality images, followed by PTL induced restoration passes over the latest restored outputs to improve overall quality in each pass. The proposed method can learn blind restoration without requiring any paired data while surpassing its limitations by leveraging progressive learning and fine tuning strategies to minimize distortions and preserve critical retinal features. To evaluate PTL's effectiveness on multi pass restoration, we conducted experiments on DeepDRiD, a large scale fundus imaging dataset specifically curated for diabetic retinopathy detection. Our result demonstrates state of the art performance, showcasing PTL's potential as a superior approach to iterative image quality restoration.
Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data
Authors: Ziwang Xu, Lanqing Guo, Satoshi Tsutsui, Shuyan Zhang, Alex C. Kot, Bihan Wen
Date: 2025-04-14 00:00:00
Tasks: Colorization, Knowledge Distillation

Staining is essential in cell imaging and medical diagnostics but poses significant challenges, including high cost, time consumption, labor intensity, and irreversible tissue alterations. Recent advances in deep learning have enabled digital staining through supervised model training. However, collecting large-scale, perfectly aligned pairs of stained and unstained images remains difficult. In this work, we propose a novel unsupervised deep learning framework for digital cell staining that reduces the need for extensive paired data using knowledge distillation. We explore two training schemes: (1) unpaired and (2) paired-but-misaligned settings. For the unpaired case, we introduce a two-stage pipeline, comprising light enhancement followed by colorization, as a teacher model. Subsequently, we obtain a student staining generator through knowledge distillation with hybrid non-reference losses. To leverage the pixel-wise information between adjacent sections, we further extend to the paired-but-misaligned setting, adding the Learning to Align module to utilize pixel-level information. Experiment results on our dataset demonstrate that our proposed unsupervised deep staining method can generate stained images with more accurate positions and shapes of the cell targets in both settings. Compared with competing methods, our method achieves improved results both qualitatively and quantitatively (e.g., NIQE and PSNR).We applied our digital staining method to the White Blood Cell (WBC) dataset, investigating its potential for medical applications.
OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation
Authors: Justin Namuk Kim, Yiqiao Liu, Rajath Soans, Keith Persson, Sarah Halek, Michal Tomaszewski, Jianda Yuan, Gregory Goldmacher, Antong Chen
Date: 2025-04-13 00:00:00
Tasks: Computational Efficiency, Lesion Segmentation, Mamba, Segmentation

Accurate segmentation of longitudinal CT scans is important for monitoring tumor progression and evaluating treatment responses. However, existing 3D segmentation models solely focus on spatial information. To address this gap, we propose OmniMamba4D, a novel segmentation model designed for 4D medical images (3D images over time). OmniMamba4D utilizes a spatio-temporal tetra-orientated Mamba block to effectively capture both spatial and temporal features. Unlike traditional 3D models, which analyze single-time points, OmniMamba4D processes 4D CT data, providing comprehensive spatio-temporal information on lesion progression. Evaluated on an internal dataset comprising of 3,252 CT scans, OmniMamba4D achieves a competitive Dice score of 0.682, comparable to state-of-the-arts (SOTA) models, while maintaining computational efficiency and better detecting disappeared lesions. This work demonstrates a new framework to leverage spatio-temporal information for longitudinal CT lesion segmentation.
Enhancing Wide-Angle Image Using Narrow-Angle View of the Same Scene
Authors: Hussain Md. Safwan, Mahbub Islam Mahim
Date: 2025-04-13 00:00:00
Tasks: Generative Adversarial Network

A common dilemma while photographing a scene is whether to capture it at a wider angle, allowing more of the scene to be covered but in less detail or to click in a narrow angle that captures better details but leaves out portions of the scene. We propose a novel method in this paper that infuses wider shots with finer quality details that is usually associated with an image captured by the primary lens by capturing the same scene using both narrow and wide field of view (FoV) lenses. We do so by training a Generative Adversarial Network (GAN)-based model to learn to extract the visual quality parameters from a narrow-angle shot and to transfer these to the corresponding wide-angle image of the scene using residual connections and an attention-based fusion module. We have mentioned in details the proposed technique to isolate the visual essence of an image and to transfer it into another image. We have also elaborately discussed our implementation details and have presented the results of evaluation over several benchmark datasets and comparisons with contemporary advancements in the field.
Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance
Authors: Jiahua Xu, Dawei Zhou, Lei Hu, Zaiyi Liu, Nannan Wang, Xinbo Gao
Date: 2025-04-13 00:00:00
Tasks: Clinical Knowledge, Language Modeling, Language Modelling

Multimodal medical images play a crucial role in the precise and comprehensive clinical diagnosis. Diffusion model is a powerful strategy to synthesize the required medical images. However, existing approaches still suffer from the problem of anatomical structure distortion due to the overfitting of high-frequency information and the weakening of low-frequency information. Thus, we propose a novel method based on dynamic frequency balance and knowledge guidance. Specifically, we first extract the low-frequency and high-frequency components by decomposing the critical features of the model using wavelet transform. Then, a dynamic frequency balance module is designed to adaptively adjust frequency for enhancing global low-frequency features and effective high-frequency details as well as suppressing high-frequency noise. To further overcome the challenges posed by the large differences between different medical modalities, we construct a knowledge-guided mechanism that fuses the prior clinical knowledge from a visual language model with visual features, to facilitate the generation of accurate anatomical structures. Experimental evaluations on multiple datasets show the proposed method achieves significant improvements in qualitative and quantitative assessments, verifying its effectiveness and superiority.
Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network
Authors: Ruiwen Ding, Lin Li, Rajath Soans, Tosha Shah, Radha Krishnan, Marc Alexander Sze, Sasha Lukyanov, Yash Deshpande, Antong Chen
Date: 2025-04-13 00:00:00
Tasks: Graph Neural Network, Multiple Instance Learning, whole slide images

Inflammatory bowel disease (IBD) involves chronic inflammation of the digestive tract, with treatment options often burdened by adverse effects. Identifying biomarkers for personalized treatment is crucial. While immune cells play a key role in IBD, accurately identifying ulcer regions in whole slide images (WSIs) is essential for characterizing these cells and exploring potential therapeutics. Multiple instance learning (MIL) approaches have advanced WSI analysis but they lack spatial context awareness. In this work, we propose a weakly-supervised model called DomainGCN that employs a graph convolution neural network (GCN) and incorporates domain-specific knowledge of ulcer features, specifically, the presence of epithelium, lymphocytes, and debris for WSI-level ulcer prediction in IBD. We demonstrate that DomainGCN outperforms various state-of-the-art (SOTA) MIL methods and show the added value of domain knowledge.
Computationally iterative methods for salt-and-pepper denoising
Authors: Jianwei Ke
Date: 2025-04-13 00:00:00
Tasks: Denoising, Image Restoration

Image restoration refers to the process of reconstructing noisy, destroyed, or missing parts of an image, which is an ill-posed inverse problem. A specific regularization term and image degradation are typically assumed to achieve well-posedness. Based on the underlying assumption, an image restoration problem can be modeled as a linear or non-linear optimization problem with or without regularization, which can be solved by iterative methods. In this work, we propose two different iterative methods by linearizing a system of non-linear equations and coupling them with a two-phase iterative framework. The qualitative and quantitative experimental results demonstrate the correctness and efficiency of the proposed methods.
seg2med: a bridge from artificial anatomy to multimodal medical images
Authors: Zeyu Yang, Zhilin Chen, Yipeng Sun, Anika Strittmatter, Anish Raj, Ahmad Allababidi, Johann S. Rink, Frank G. Zöllner
Date: 2025-04-12 00:00:00
Tasks: Anatomy, Data Augmentation, Denoising, Diagnostic, Image Generation, Medical Image Generation, SSIM

We present seg2med, a modular framework for anatomy-driven multimodal medical image synthesis. The system integrates three components to enable high-fidelity, cross-modality generation of CT and MR images based on structured anatomical priors. First, anatomical maps are independently derived from three sources: real patient data, XCAT digital phantoms, and synthetic anatomies created by combining organs from multiple patients. Second, we introduce PhysioSynth, a modality-specific simulator that converts anatomical masks into prior volumes using tissue-dependent parameters (e.g., HU, T1, T2, proton density) and modality-specific signal models. It supports simulation of CT and multiple MR sequences including GRE, SPACE, and VIBE. Third, the synthesized anatomical priors are used to train 2-channel conditional denoising diffusion models, which take the anatomical prior as structural condition alongside the noisy image, enabling generation of high-quality, structurally aligned images. The framework achieves SSIM of 0.94 for CT and 0.89 for MR compared to real data, and FSIM of 0.78 for simulated CT. The generative quality is further supported by a Frechet Inception Distance (FID) of 3.62 for CT synthesis. In modality conversion, seg2med achieves SSIM of 0.91 for MR to CT and 0.77 for CT to MR. Anatomical fidelity evaluation shows synthetic CT achieves mean Dice scores above 0.90 for 11 key abdominal organs, and above 0.80 for 34 of 59 total organs. These results underscore seg2med's utility in cross-modality synthesis, data augmentation, and anatomy-aware medical AI.
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention
Authors: Yonghao Huang, Leiting Chen, Chuan Zhou
Date: 2025-04-12 00:00:00
Tasks: Brain Tumor Segmentation, Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Due to the success of CNN-based and Transformer-based models in various computer vision tasks, recent works study the applicability of CNN-Transformer hybrid architecture models in 3D multi-modality medical segmentation tasks. Introducing Transformer brings long-range dependent information modeling ability in 3D medical images to hybrid models via the self-attention mechanism. However, these models usually employ fixed receptive fields of 3D volumetric features within each self-attention layer, ignoring the multi-scale volumetric lesion features. To address this issue, we propose a CNN-Transformer hybrid 3D medical image segmentation model, named TMA-TransBTS, based on an encoder-decoder structure. TMA-TransBTS realizes simultaneous extraction of multi-scale 3D features and modeling of long-distance dependencies by multi-scale division and aggregation of 3D tokens in a self-attention layer. Furthermore, TMA-TransBTS proposes a 3D multi-scale cross-attention module to establish a link between the encoder and the decoder for extracting rich volume representations by exploiting the mutual attention mechanism of cross-attention and multi-scale aggregation of 3D tokens. Extensive experimental results on three public 3D medical segmentation datasets show that TMA-TransBTS achieves higher averaged segmentation results than previous state-of-the-art CNN-based 3D methods and CNN-Transform hybrid 3D methods for the segmentation of 3D multi-modality brain tumors.
PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2
Authors: Mingyang Zhu, Yinting Liu, Mingyu Li, Jiacheng Wang
Date: 2025-04-12 00:00:00
Tasks: Domain Adaptation, Image Segmentation, Segmentation, Semantic Segmentation

Current methods for pathology image segmentation typically treat 2D slices independently, ignoring valuable cross-slice information. We present PathSeqSAM, a novel approach that treats 2D pathology slices as sequential video frames using SAM2's memory mechanisms. Our method introduces a distance-aware attention mechanism that accounts for variable physical distances between slices and employs LoRA for domain adaptation. Evaluated on the KPI Challenge 2024 dataset for glomeruli segmentation, PathSeqSAM demonstrates improved segmentation quality, particularly in challenging cases that benefit from cross-slice context. We have publicly released our code at https://github.com/JackyyyWang/PathSeqSAM.
Remote Sensing Based Crop Health Classification Using NDVI and Fully Connected Neural Networks
Authors: J. Judith, R. Tamilselvi, M. Parisa Beham, S. Sathiya Pandiya Lakshmi, Alavikunhu Panthakkan, Saeed Al Mansoori, Hussain Al Ahmad
Date: 2025-04-11 00:00:00

Accurate crop health monitoring is not only essential for improving agricultural efficiency but also for ensuring sustainable food production in the face of environmental challenges. Traditional approaches often rely on visual inspection or simple NDVI measurements, which, though useful, fall short in detecting nuanced variations in crop stress and disease conditions. In this research, we propose a more sophisticated method that leverages NDVI data combined with a Fully Connected Neural Network (FCNN) to classify crop health with greater precision. The FCNN, trained using satellite imagery from various agricultural regions, is capable of identifying subtle distinctions between healthy crops, rust-affected plants, and other stressed conditions. Our approach not only achieved a remarkable classification accuracy of 97.80% but it also significantly outperformed conventional models in terms of precision, recall, and F1-scores. The ability to map the relationship between NDVI values and crop health using deep learning presents new opportunities for real-time, large-scale monitoring of agricultural fields, reducing manual efforts, and offering a scalable solution to address global food security.
Artificial Intelligence Augmented Medical Imaging Reconstruction in Radiation Therapy
Authors: Di Xu
Date: 2025-04-10 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction, Representation Learning

Efficiently acquired and precisely reconstructed imaging are crucial to the success of modern radiation therapy (RT). Computed tomography (CT) and magnetic resonance imaging (MRI) are two common modalities for providing RT treatment planning and delivery guidance/monitoring. In recent decades, artificial intelligence (AI) has emerged as a powerful and widely adopted technique across various fields, valued for its efficiency and convenience enabled by implicit function definition and data-driven feature representation learning. Here, we present a series of AI-driven medical imaging reconstruction frameworks for enhanced radiotherapy, designed to improve CT image reconstruction quality and speed, refine dual-energy CT (DECT) multi-material decomposition (MMD), and significantly accelerate 4D MRI acquisition.
Multi-Orbiter Continuous Lunar Beaming
Authors: Baris Donmez, Yanni Jiwan-Mercier, Sebastien Loranger, Gunes Karabulut Kurt
Date: 2025-04-15 00:00:00

In this work, free-space optics-based continuous wireless power transmission between multiple low lunar orbit satellites and a solar panel on the lunar rover located at the lunar south pole are investigated based on the time-varying harvested power and overall system efficiency metrics. The performances are compared between a solar panel with the tracking ability and a fixed solar panel that induces \textit{the cosine effect} due to the time-dependent angle of incidence (AoI). In our work, the Systems Tool Kit (STK) high-precision orbit propagator, which calculates the ephemeris data precisely, is utilized. Interestingly, orbiter deployments in constellations change significantly during a Moon revolution; thus, short-duration simulations cannot be used straightforwardly. In our work, many satellite configurations are assessed to be able to find a Cislunar constellation that establishes a continuous line-of-sight (LoS) between the solar panel and at least a single LLO satellite. It is found that 40-satellite schemes enable the establishment of a continuous WPT system model. Besides, a satellite selection method (SSM) is introduced so that only the best LoS link among multiple simultaneous links from multiple satellites will be active for optimum efficiency. Our benchmark system of a 40-satellite quadruple orbit scheme is compared with 30-satellite and a single satellite schemes based on the average harvested powers and overall system efficiencies 27.3 days so the trade-off options can be assessed from the multiple Cislunar models. The outcomes show that the average system efficiencies of single, 30-satellite, and 40-satellite schemes are 2.84%, 32.33%, and 33.29%, respectively, for the tracking panel and 0.97%, 18.33%, and 20.44%, respectively, for the fixed solar panel case.