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Medical Imaging 2025: image Processing

作者： Gort, Pieter C. Claessens, Cris H.B. De With, Peter H.N. van der Sommen, Fons Video Coding and Architectures Research Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9781510685901

Artificial intelligence (AI) holds great potential for assisting physicians in medical image analysis, yet current models provide predictions without transparent explanations of their decision-making process. Explainable AI (XAI) aims to address this issue by providing tools to enhance model interpretability. However, existing XAI methods, such as post-hoc explanation techniques, often produce coarse heatmaps that lack the precision needed for medical applications. In contrast, self-explaining models like prototype networks offer a promising approach by learning characteristic image patterns, called prototypes. These prototypes could aid physicians in their diagnostic process. In this work, we evaluate the interpretability of prototype networks in the context of medical image classification using a mammography and a colorectal polyp (CP) dataset. We assess the quality and relevance of the learned prototypes by comparing them to domain-specific knowledge and physician-annotated bounding boxes. Our key contribution involves linking these prototypes to textual descriptions of CPs. Our experiments demonstrate that learned prototypes visually correspond to clinically relevant regions of interest (ROIs), obtaining a mean intersection over union (IoU) of 0.25 ± 0.29. Challenges remain in improving spatial accuracy and linking prototypes to textual descriptions. We discuss these limitations and propose future directions for integrating prototype networks into clinical workflows, contributing to the development of transparent and trustworthy XAI systems for clinical decision support. © 2025 SPIE.

关键词： Mammography

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Blind Motion Deblurring With Pixel-Wise Kernel Estimation via Kernel Prediction Networks

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2023年 9卷 928-943页

作者： Carbajal, Guillermo Vitoria, Patricia Lezama, Jose Muse, Pablo Universidad de la República Department of Electrical Engineering Montevideo11300 Uruguay Universitat Pompeu Fabra Image Processing Group Barcelona08002 Spain

In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. For this reason, approaches that explicitly use a forward degradation model received significantly less attention. However, a well-defined specification of the blur genesis, as an intermediate step, promotes the generalization and explainability of the method. Towards this goal, we propose a learning-based motion deblurring method based on dense non-uniform motion blur estimation followed by a non-blind deconvolution approach. Specifically, given a blurry image, a first network estimates the dense per-pixel motion blur kernels using a lightweight representation composed of a set of image-adaptive basis motion kernels and the corresponding mixing coefficients. Then, a second network trained jointly with the first one, unrolls a non-blind deconvolution method using the motion kernel field estimated by the first network. The model-driven aspect is further promoted by training the networks on sharp/blurry pairs synthesized according to a convolution-based, non-uniform motion blur degradation model. Qualitative and quantitative evaluation shows that the kernel prediction network produces accurate motion blur estimates, and that the deblurring pipeline leads to restorations of real blurred images that are competitive or superior to those obtained with existing end-to-end deep learning-based methods. © 2015 IEEE.

关键词： image reconstruction

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Diffusion-based lung nodule synthesis for advanced evaluation of deep learning models

Diffusion-based lung nodule synthesis for advanced evaluatio...

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Medical Imaging 2025: image Processing

作者： Claessens, Cris H.B. Caetano, Francisco van der Wulp, Kasper Ewals, Lotte J.S. De With, Peter H.N. van der Sommen, Fons Video Coding and Architectures Research Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Radiology Catharina Hospital Eindhoven Eindhoven Netherlands

ISBN: (纸本)9781510685901

The accurate detection of lung nodules in computed tomography (CT) scans is critical for the early diagnosis and treatment of lung cancer. Despite the advances of computer aided detection algorithms for nodule detection, biases in training data often compromise the generalization of deep learning models trained for this task. To address this, we present a novel methodology for synthesizing realistic lung nodules, using a fine-tuned version of the Instruct Pix2Pix framework, based on Stable Diffusion. The proposed approach enables precise control over the size and placement of the nodule within CT slices, facilitating the creation of synthetic datasets tailored for systematic bias evaluation and model validation. By generating paired CT slices with and without nodules, we provide a supervised baseline for training the diffusion model. This work demonstrates the realism of the created nodules through a clinician evaluation study, where observers often misclassified synthetic nodules as real. In fact, clinicians have mistaken synthesized nodules for real nodules in 41% of cases, a rate close to the probability level of 50%, underscoring the high fidelity of the generated images. This pioneering work shows the applicability of diffusion models for evaluating deep learning models and opens doors for future research to extend this methodology to 3D and high-quality scans. The resulting dataset and code will be made publicly available to promote further research in this domain. © 2025 SPIE.

关键词： Photointerpretation

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Self-Supervised Learning Using Nonlinear Dependence

arXiv

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arXiv 2025年

作者： Sepanj, M. Hadi Ghojogh, Benyamin Fieguth, Paul Vision and Image Processing Group Systems Design Engineering University of Waterloo ON Canada Department of Electrical and Computer Engineering University of Waterloo ON Canada

Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear correlations, they often neglect the intricate relations between samples and the nonlinear dependencies inherent in complex data. In this paper, we introduce Correlation-Dependence Self-Supervised Learning (CDSSL), a novel framework that unifies and extends existing SSL paradigms by integrating both linear correlations and nonlinear dependencies, encapsulating sample-wise and feature-wise interactions. Our approach incorporates the Hilbert-Schmidt Independence Criterion (HSIC) to robustly capture nonlinear dependencies within a Reproducing Kernel Hilbert Space, enriching representation learning. Experimental evaluations on diverse benchmarks demonstrate the efficacy of CDSSL in improving representation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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2023 International Conference on Digital image Computing: Techniques and Applications, DICTA 2023

作者： Zhou, Jieming Zhang, Tong Fang, Pengfei Petersson, Lars Harandi, Mehrtash Australian National University College of Engineering and Computer Science Canberra Australia Image and Visual Representation Laboratory Epfl Lausanne Switzerland Imaging and Computer Vision Group Csiro Canberra Australia Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350382204

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs' designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model's performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks. © 2023 IEEE.

关键词： Graph neural networks

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Dermoscopic image Classification Using Attention Mechanism and Ensemble Learning Approaches

Dermoscopic Image Classification Using Attention Mechanism a...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Huang, Shanchuan Lei, Hongwei Jin, Liuhan Yang, Jinzhu Jiang, Tao Yao, Yudong Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Stevens Institute of Technology Department of Electrical and Computer Engineering United States Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings. © 2023 IEEE.

关键词： Attention mechanism Ensemble learning image classification Late fusion Skin Cancer

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WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal

引用

NMR in Biomedicine 2025年第6期38卷

作者： Merkofer, Julian P. van de Sande, Dennis M. J. Amirrajab, Sina Min Nam, Kyung van Sloun, Ruud J. G. Bhogal, Alex A. Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands High Field Research Group Center for Image Sciences University Medical Center Utrecht Utrecht Netherlands

Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the quantification of low-concentration metabolites, limiting MRS reliability. This paper introduces wavelet analysis-based neural decomposition (WAND), a novel data-driven method designed to decompose MRS signals into their constituent components: metabolite-specific signals, baseline, and artifacts. WAND takes advantage of the enhanced separability of these components within the wavelet domain. The method employs a neural network, specifically a U-Net architecture, trained to predict masks for wavelet coefficients obtained through the continuous wavelet transform. These masks effectively isolate desired signal components in the wavelet domain, which are then inverse-transformed to obtain separated signals. Notably, an artifact mask is created by inverting the sum of all known signal masks, enabling WAND to capture and remove even unpredictable artifacts. The effectiveness of WAND in achieving accurate decomposition is demonstrated through numerical evaluations using simulated spectra. Furthermore, WAND's artifact removal capabilities significantly enhance the quantification accuracy of linear combination model fitting. The method's robustness is further validated using data from the 2016 MRS Fitting Challenge and in vivo experiments. © 2025 The Author(s). NMR in Biomedicine published by John Wiley & Sons Ltd.

关键词： Nuclear magnetic resonance spectroscopy

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Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer:a comprehensive comparative study

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Intelligent Medicine 2024年第2期4卷 114-127页

作者： Ao Chen Chen Li Md Mamunur Rahaman Yudong Yao Haoyuan Chen Hechen Yang Peng Zhao Weiming Hu Wanli Liu Shuojia Zou Ning Xu Marcin Grzegorzek Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110169China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Arts and Design Liaoning Petrochemical UniversityFushunLiaoning 113001China Institute of Medical Informatics University of LübeckLüebeckGermany

Background With the gradual increase of infertility in the world,among which male sperm problems are the main factor for infertility,more and more couples are using computer-assisted sperm analysis(CASA)to assist in the analysis and treatment of ***,the rapid development of deep learning(DL)has led to strong results in image classification ***,the classification of sperm images has not been well studied in current deep learning methods,and the sperm images are often affected by noise in practical CASA *** purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm *** The SVIA dataset is a publicly available large-scale sperm dataset containing three *** this work,we used subset-C,which provides more than 125,000 independent images of sperms and impurities,including 121,401 sperm images and 4,479 impurity *** investigate the anti-noise robustness of deep learning classification methods applied on sperm images,we conducted a comprehensive comparative study of sperm images using many convolutional neural network(CNN)and visual transformer(VT)deep learning methods to find the deep learning model with the most stable anti-noise *** This study proved that VT had strong robustness for the classification of tiny object(sperm and impurity)image datasets under some types of conventional noise and some adversarial *** particular,under the influence of Poisson noise,accuracy changed from 91.45%to 91.08%,impurity precison changed from 92.7%to 91.3%,impurity recall changed from 88.8%to 89.5%,and impurity F1-score changed 90.7%to 90.4%.Meanwhile,sperm precision changed from 90.9%to 90.5%,sperm recall changed from 92.5%to 93.8%,and sperm F1-score changed from 92.1%to 90.4%.Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods;the robustness with regard to noise of VT methods based

关键词： Computer-assisted sperm analysis Anti-noise Robustness Deep learning .image classification Sperm image Conventional noise Adversarial attacks Convolutional neural network Visual transformer

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Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks

arXiv

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arXiv 2023年

作者： Carbajal, Guillermo Vitoria, Patricia Lezama, José Musé, Pablo The Department of Electrical Engineering Universidad de la República Uruguay The Image Processing Group Universitat Pompeu Fabra Spain

关键词： Pixels

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Scaling up self-supervised learning for improved surgical foundation models

arXiv

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arXiv 2025年

作者： Jaspers, Tim J.M. de Jong, Ronald L.P.D. Li, Yiping Kusters, Carolus H.J. Bakker, Franciscus H.A. van Jaarsveld, Romy C. Kuiper, Gino M. van Hillegersberg, Richard Ruurda, Jelle P. Brinkman, Willem M. Pluim, Josien P.W. de With, Peter H.N. Breeuwer, Marcel Khalil, Yasmina Al van der Sommen, Fons Department of Electrical Engineering Video Coding & Architectures Eindhoven University of Technology Eindhoven Netherlands Department of Biomedical Engineering Medical Image Analysis Eindhoven University of Technology Eindhoven Netherlands Department of Surgery University Medical Center Utrecht Utrecht Netherlands Department of Oncological Urology University Medical Center Utrecht Utrecht Netherlands Department of Urology Catharina Hospital Eindhoven Netherlands

Foundation models have revolutionized computer vision by achieving vastly superior performance across diverse tasks through large-scale pretraining on extensive datasets. However, their application in surgical computer vision has been limited. This study addresses this gap by introducing SurgeNetXL, a novel surgical foundation model that sets a new benchmark in surgical computer vision. Trained on the largest reported surgical dataset to date, comprising over 4.7 million video frames, SurgeNetXL achieves consistent top-tier performance across six datasets spanning four surgical procedures and three tasks, including semantic segmentation, phase recognition, and critical view of safety (CVS) classification. Compared with the best-performing surgical foundation models, SurgeNetXL shows mean improvements of 2.4, 9.0, and 12.6 percent for semantic segmentation, phase recognition, and CVS classification, respectively. Additionally, SurgeNetXL outperforms the best-performing imageNet-based variants by 14.4, 4.0, and 1.6 percent in the respective tasks. In addition to advancing model performance, this study provides key insights into scaling pretraining datasets, extending training durations, and optimizing model architectures specifically for surgical computer vision. These findings pave the way for improved generalizability and robustness in data-scarce scenarios, offering a comprehensive framework for future research in this domain. All models and a subset of the SurgeNetXL dataset, including over 2 million video frames, are publicly available at: https://***/TimJaspers0801/SurgeNet. © 2025, CC BY.

关键词： Self-supervised learning

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