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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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PRS-Net: Planar reflective symmetry detection net for 3D models

arXiv

引用

arXiv 2019年

作者： Gao, Lin Zhang, Ling-Xiao Meng, Hsien-Yu Ren, Yi-Hui Lai, Yu-Kun Kobbelt, Leif Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Maryland College ParkMD United States Visual Computing Group School of Computer Science and Informatics Cardiff University Wales United Kingdom Institute for Computer Graphics and Multimedia RWTH Aachen University Aachen Germany

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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Biomedical image analysis competitions: The state of current participation practice

arXiv

引用

arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers

arXiv

引用

arXiv 2025年

作者： Wang, Meng Lin, Tian Hou, Qingshan Lin, Aidi Wang, Jingcheng Peng, Qingsheng Nguyen, Truong X. Fang, Danqi Zou, Ke Xu, Ting Xue, Cancan Quek, Ten Cheer Yu, Qinkai Liu, Minxin Zhou, Hui Xiao, Zixuan He, Guiqin Liang, Huiyu Shi, Tingkun Chen, Man Liu, Linna Peng, Yuanyuan Wang, Lianyu Hu, Qiuming Chen, Junhong Zhang, Zhenhua Chen, Cheng Zhao, Yitian Liu, Dianbo Wu, Jianhua Chen, Xinjian Zhang, Changqing Nguyen, Triet Thanh Meng, Yanda Zheng, Yalin Tham, Yih Chung Cheung, Carol Y. Fu, Huazhu Chen, Haoyu Cheng, Ching-Yu Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore119228 Singapore Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore119228 Singapore Joint Shantou International Eye Center Shantou University The Chinese University of Hong Kong Guangdong Shantou515041 China Shantou University Medical College Guangdong Shantou515041 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Big Vision Medical Technology Ltd. Suzhou215011 China Singapore Eye Research Institute Singapore National Eye Centre Singapore169856 Singapore Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong 999077 Hong Kong Binh Dinh Eye Hospital Viet Nam Department of Computer Science University of Exeter ExeterEX4 4RN United Kingdom University of Science and Technology Hospital Guangdong Shenzhen518000 China Shenzhen Qianhai Shekou Free Trade Zone Hospital Guangdong Shenzhen518000 China Department of Ophthalmology Meizhou People’s Hospital Meizhou Academy of Medical Sciences Guangdong Meizhou514000 China Department of Ophthalmology Yeungnam University College of Medicine Daegu42417 Korea Republic of Aier Eye Hospital of Wuhan University Hubei Wuhan430000 China School of Biomedical Engineering Anhui Medical University Anhui Hefei230032 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211100 China Guangxi Jingliang Eye Hospital Guangxi Nanning530021 China Puning People’s Hospital Guangdong Jieyang522000 China Shandong Qingdao266042 China Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315201 China School of Electronics and Information Engineering Soochow University

Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, but current models typically require retraining when deployed across different clinical centers, limiting their widespread adoption. We introduce GlobeReady, a clinician-friendly AI platform that enables ocular disease diagnosis without retraining/fine-tuning or technical expertise. GlobeReady achieves high accuracy across imaging modalities: 93.9–98.5% for an 11-category fundus photo dataset and 87.2–92.7% for a 15-category OCT dataset. Through training-free local feature augmentation, it addresses domain shifts across centers and populations, reaching an average accuracy of 88.9% across five centers in China, 86.3% in Vietnam, and 90.2% in the UK. The built-in confidence-quantifiable diagnostic approach further boosted accuracy to 94.9–99.4% (fundus) and 88.2–96.2% (OCT), while identifying out-of-distribution cases at 86.3% (49 CFP categories) and 90.6% (13 OCT categories). Clinicians from multiple countries rated GlobeReady highly (average 4.6 out of 5) for its usability and clinical relevance. These results demonstrate GlobeReady’s robust, scalable diagnostic capability and potential to support ophthalmic care without technical barriers. © 2025, CC0.

关键词： Ophthalmology

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Data painter: A tool for colormap interaction 35

Data painter: A tool for colormap interaction

引用

35th Annual Conference on computer Graphics and visual computing, CGVC 2017

作者： Nagoor, O.H. Borgo, R. Jones, M.W. Swansea University Department of Computer Science Visual Computing United Kingdom King’s College London United Kingdom

ISBN: (纸本)9783038680505

The choice of a mapping from data to color should involve careful consideration in order to maximize the user understanding of the underlying data. It is desirable for features within the data to be visually separable and identifiable. Current practice involves selecting a mapping from predefined colormaps or coding specific colormaps using software such as MATLAB. The purposes of this paper are to introduce interactive operations for colormaps that enable users to create more visually distinguishable pixel based visualizations, and to describe our tool, Data Painter, that provides a fast, easy to use framework for defining these color mappings. We demonstrate the use of the tool to create colormaps for various application areas and compare to existing color mapping methods. We present a new objective measure to evaluate their efficacy. © 2017 The Author(s) Eurographics Proceedings © 2017 The Eurographics Association.

关键词： Mapping

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An automated system for epilepsy detection using EEG brain signals based on deep learning approach

arXiv

引用

arXiv 2018年

作者： Ullah, Ihsan Hussain, Muhammad Qazi, Emad-Ul-Haq Aboalsamh, Hatim Insight Centre for Data Analytics National University of Ireland Galway Ireland Visual Computing Lab Department of Computer Science College of Computer andInformation Sciences King Saud University Riyadh Saudi Arabia

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal;the maximum accuracy for this case by the state-of-the-art-methods is 97±1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1±0.9% on the University of Bonn dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurology

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On large local error accumulation in multilevel error diffusion 22

On large local error accumulation in multilevel error diffus...

引用

Color Imaging XXII: Displaying, Processing, Hardcopy, and Applications 2017

作者： Eschbach, Reiner Pedersen, Marius Norwegian Colour and Visual Computing Laboratory Faculty of Computer Science and Media Technology NTNU-Norwegian University of Science and Technology Gjøvik Norway Monroe Community College RochesterNY United States

Error diffusion is an often used method that transforms a continuous tone (multibit) image into an image of lower bit depth, most commonly into a binary output of black and white. The simplicity of the processing and the quality of the output have made error diffusion a frequently used tool. Part of the image quality is attributed to the minimization of quantization errors in the error-diffusion process. This article describes local instabilities in a color multilevel error-diffusion system that-in contrast-can lead to large local errors in the output, far exceeding the normally expected quantization errors. This can have serious negative effects specifically in connection with the design and incorporation of color calibration sheets. © 2017, Society for Imaging science and Technology.

关键词： Color

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Quality of color coding in maps for color deficient observers 21

Quality of color coding in maps for color deficient observer...

引用

Color Imaging XXI: Displaying, Processing, Hardcopy, and Applications 2016

作者： Kvitle, Anne Kristin Pedersen, Marius Nussbaum, Peter Colour and Visual Computing Laboratory Faculty of Computer Science and Media Technology Gjøvik University College Gjøvik Norway

For a color deficient observer, the quality of a map or other information design may be defined as the ability to extract features. As color is such important conveyor of information, the colors need to appear correct and be perceived in the desired and intended way. As color appearance is affected by the size of the stimuli, the task of discriminate colors may be even more difficult for a color vision deficient observer. In order to investigate the discriminability of the color coding in an official Norwegian map product, we conducted an experiment involving both color deficient and color normal observers. Also, we investigate to what extent the ability to discriminate colors is influenced by size of the visual field. The experiment revealed that the color vision deficient observers made significant more errors than the normal observers, especially when the visual angle was reduced. © 2016 Society for Imaging science and Technology.

关键词： Color

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Neural style transfer: A review

arXiv

引用

arXiv 2017年

作者： Jing, Yongcheng Yang, Yezhou Feng, Zunlei Ye, Jingwen Yu, Yizhou Song, Mingli Microsoft Visual Perception Laboratory College of Computer Science and Technology Zhejiang University Hangzhou310027 School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://***/ycjing/Neural-Style-Transfer-Papers. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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Multiscale approach for dehazing using the STRESS framework 21

Multiscale approach for dehazing using the STRESS framework

引用

Color Imaging XXI: Displaying, Processing, Hardcopy, and Applications 2016

作者： De Dravo, Vincent Jacob Whannou Hardeberg, Jon Yngve Norwegian Colour and Visual Computing Laboratory Faculty of Computer Science and Media Technology Gjøvik University College GjøvikN-2802 Norway

Models that researchers often use for the dehazing task are based on the Koschmieder law. In this article, we use the STRESS (Spatio-Temporal Retinex-inspired Envelope with Stochastic Sampling) model for the dehazing task. In our work, we demonstrate theoretically and empirically how the parameters in the STRESS framework can be set for dehazing. We then propose a new algorithm for haze removal, based on the model of the (STRESS) framework, which combines edge detection and Hidden Markov Model (HMM) to solve the problem. Experiments show that our approach yields more visibility - based on some metrics and psychophysical tests - than most of the state-of-the-art approaches. © 2016 Society for Imaging science and Technology.

关键词： Stochastic systems

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