Article
Computer Science, Artificial Intelligence
Qiming Zhang, Yufei Xu, Jing Zhang, Dacheng Tao
Summary: Vision transformers have shown promise in computer vision tasks due to their ability to model long-range dependency. However, they lack an intrinsic bias in modeling local visual structures and dealing with scale variance. This paper introduces the ViTAE transformer, which utilizes two biases and achieves superior performance on various datasets.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2023)
Review
Biology
Huiyan Jiang, Zhaoshuo Diao, Tianyu Shi, Yang Zhou, Feiyu Wang, Wenrui Hu, Xiaolin Zhu, Shijie Luo, Guoyu Tong, Yu-Dong Yao
Summary: With the advancement of deep learning in natural image classification, detection, and segmentation, deep learning-based methods have become dominant in medical image processing. They have shown great effectiveness in single lesion recognition and segmentation. However, multiple-lesion recognition is more challenging due to the little variation or wide range of lesions involved. Recent studies have explored deep learning-based algorithms to tackle this challenge.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Junlong Cheng, Shengwei Tian, Long Yu, Chengrui Gao, Xiaojing Kang, Xiang Ma, Weidong Wu, Shijia Liu, Hongchun Lu
Summary: Deep learning has shown superior performance in medical image analysis, and the proposed ResGANet model outperforms state-of-the-art backbone models in medical image tasks, providing a promising method for enhancing the feature representation of convolutional neural networks (CNNs) in the future.
MEDICAL IMAGE ANALYSIS
(2022)
Review
Radiology, Nuclear Medicine & Medical Imaging
Jiwoong J. Jeong, Amara Tariq, Tobiloba Adejumo, Hari Trivedi, Judy W. Gichoya, Imon Banerjee
Summary: This article provides a systematic review of recent GAN architectures used in medical image analysis. It offers a comprehensive overview of the latest trends in the application of GANs in clinical diagnosis and shares experiences in task-based GAN implementations.
JOURNAL OF DIGITAL IMAGING
(2022)
Article
Computer Science, Software Engineering
Xianjun Du, Hailei Wu
Summary: This paper proposes a gated aggregation network (GANet) for remote sensing image cloud detection, which efficiently extracts cloud regions using RGB preview images. The method addresses the feature fusion problem through an encoder-decoder architecture, a gated feature aggregation module, and a pyramidal attention pooling module, achieving competitive performance in cloud detection.
Article
Computer Science, Artificial Intelligence
Lihao Liu, Angelica I. Aviles-Rivero, Carola-Bibiane Schonlieb
Summary: Medical image segmentation is a crucial task in medical imaging, and this paper presents a novel unsupervised segmentation technique called CLMorph. By combining registration and contrastive learning, the proposed technique achieves improved accuracy in image segmentation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Multidisciplinary Sciences
Dongmei Zhu, Dongbo Wang
Summary: This paper mainly focuses on the application of transformers in medical image processing, including image segmentation, reconstruction, and classification. The paper summarizes the improvement mechanisms of transformers and discusses the future development prospects and challenges.
JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Soufiane Belharbi, Jerome Rony, Jose Dolz, Ismail Ben Ayed, Luke Mccaffrey, Eric Granger
Summary: This paper presents an improved weakly-supervised learning method that can simultaneously search for non-discriminative and discriminative regions and suppress unbalanced segmentations by introducing novel regularization terms. Experimental results demonstrate significant improvements over state-of-the-art methods in histology image segmentation tasks.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Mathematics, Interdisciplinary Applications
Ayesha Sohail, Mohamed Abdelsabour Fahmy, Usama Ahmad Khan
Summary: Medical imaging visualizes the diseased part inside the patient's body using images, relying on various scientific and technological disciplines. This paper introduces a new hybrid machine learning approach to analyze medical images, presenting a novel algorithm for studying breast cancer images. The algorithm follows step-by-step stages to process, analyze, and classify the images. This research is significant for the field of particle physics imaging.
COMPUTATIONAL PARTICLE MECHANICS
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Havaei, Ximeng Mao, Yiping Wang, Qicheng Lao
Summary: Synthetic medical image generation using Conditional Adversarial Generative Networks shows great potential for improving healthcare, but current methods lack flexibility and control over the generated images. The proposed DRAI framework achieves better performance and superior style-content disentanglement compared to baseline models.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Biraja Ghoshal, Allan Tucker, Bal Sanghera, Wai Lup Wong
Summary: Deep learning has made remarkable progress in medical image analysis, but current methods focus solely on accuracy of point predictions without considering the quality of outputs. This article proposes an uncertainty estimation framework, MC-DropWeights, to approximate Bayesian inference in DL by applying a Bernoulli distribution to model weights. By decomposing predictive probabilities into two main types of uncertainty and addressing mode collapse in variational inference, the MC-DropWeights method demonstrates improved estimation of uncertainty quality in image classification.
COMPUTATIONAL INTELLIGENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Dwarikanath Mahapatra, Alexander Poellinger, Ling Shao, Mauricio Reyes
Summary: In this article, we propose a novel sample selection methodology called IDEAL based on deep features for medical image analysis, which can improve system performance and reduce expert interactions. By leveraging information from interpretability saliency maps, a self-supervised learning approach is used to train a classifier to identify the most informative samples in a given batch of images. Experimental results demonstrate that the proposed approach outperforms other methods in lung disease classification and histopathology image segmentation tasks, showing the potential of using interpretability information for sample selection in active learning systems.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Interdisciplinary Applications
Xu Chen, Chunfeng Lian, Li Wang, Hannah Deng, Tianshu Kuang, Steve Fung, Jaime Gateno, Pew-Thian Yap, James J. Xia, Dinggang Shen
Summary: An anatomy-regularized representation learning approach is proposed for segmentation-oriented cross-modality image synthesis, showing superiority in comparison with state-of-the-art cross-modality medical image segmentation methods.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Review
Computer Science, Information Systems
Irena Galic, Marija Habijan, Hrvoje Leventic, Kresimir Romic
Summary: This work provides an overview of fundamental concepts, state-of-the-art models, and publicly available datasets in the field of medical imaging, with a focus on the application of deep learning methods. It also discusses current research conducted in various medical imaging areas, challenges faced, and future research directions.
Article
Computer Science, Artificial Intelligence
Hui Tang, Xiatian Zhu, Ke Chen, Kui Jia, C. L. Philip Chen
Summary: Unsupervised domain adaptation (UDA) aims to learn classification models that can make predictions for unlabeled data in a target domain. Existing UDA methods focus on learning domain-aligned features, but there is a potential risk of damaging the intrinsic data structures of target discrimination. In this study, a constrained clustering method based on the assumption of structural similarity across domains is proposed to address this issue. By imposing structural regularization, the proposed method outperforms existing methods in both inductive and transductive settings.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng
Summary: This study proposes a framework based on hybrid 2D-3D convolutional neural networks for obtaining continuous 3D retinal layer surfaces from OCT volumes. The framework works well with both full and sparse annotations and utilizes alignment displacement vectors and layer segmentation to align the B-scans and segment the layers. Experimental results show that the framework outperforms state-of-the-art 2D deep learning methods in terms of layer segmentation accuracy and cross-B-scan 3D continuity.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Simon Oxenford, Ana Sofia Rios, Barbara Hollunder, Clemens Neudorfer, Alexandre Boutet, Gavin J. B. Elias, Jurgen Germann, Aaron Loh, Wissam Deeb, Bryan Salvato, Leonardo Almeida, Kelly D. Foote, Robert Amaral, Paul B. Rosenberg, David F. Tang-Wai, David A. Wolk, Anna D. Burke, Marwan N. Sabbagh, Stephen Salloway, M. Mallar Chakravarty, Gwenn S. Smith, Constantine G. Lyketsos, Michael S. Okun, William S., Zoltan Mari, Francisco A. Ponce, Andres Lozano, Wolf-Julian Neumann, Bassam Al-Fatly, Andreas Horn
Summary: Spatial normalization is a method to map subject brain images to an average template brain, allowing comparison of brain imaging results. We introduce a novel tool called WarpDrive, which enables manual refinements of image alignment after automated registration. The tool improves accuracy of data representation and aids in understanding patient outcomes.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt
Summary: This study presents interpretable machine learning models for predicting the diagnosis, management, and severity of suspected appendicitis using ultrasound images. The proposed models utilize concept bottleneck models (CBM) that facilitate interpretation and intervention by clinicians, without compromising performance or requiring time-consuming image annotation.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W. Papiez
Summary: This article introduces a new method for medical image registration, which utilizes a separable motion backbone and a residual aligner module to better handle the discontinuous motion of multiple neighboring objects. The proposed method achieves excellent registration results on abdominal CT scans and lung CT scans.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, Zhilun Chen, Bin Pu, Shengli Li, Kenli Li
Summary: This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, simulating the diagnostic workflow of radiologists. By interpreting image characteristics and modeling temporal contextual information, the efficiency and generalizability of the diagnosis can be improved.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Summary: This paper introduces DeepSSM, a deep learning-based framework for image-to-shape modeling. By learning the functional mapping from images to low-dimensional shape descriptors, DeepSSM can directly infer statistical representation of anatomy from 3D images. Compared to traditional methods, DeepSSM eliminates the need for heavy manual preprocessing and segmentation, and significantly improves computational time.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Florentin Liebmann, Marco von Atzigen, Dominik Stutz, Julian Wolf, Lukas Zingg, Daniel Suter, Nicola A. Cavalcanti, Laura Leoty, Hooman Esfandiari, Jess G. Snedeker, Martin R. Oswald, Marc Pollefeys, Mazda Farshad, Philipp Furnstahl
Summary: This study presents a marker-less approach for automatic registration and real-time navigation of lumbar spinal fusion surgery using a deep neural network, avoiding radiation exposure and surgical errors. The method was validated on an ex-vivo surgery and a public dataset.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Piyush Tiwary, Kinjawl Bhattacharyya, A. P. Prathosh
Summary: Domain shift refers to the change of distributional characteristics between training and testing datasets, leading to performance drop. For medical image tasks, domain shift can be caused by changes in imaging modalities, devices, and staining mechanisms. Existing approaches based on generative models suffer from training difficulties and lack of diversity. In this paper, the authors propose the use of energy-based models (EBMs) for unpaired image-to-image translation in medical images. The proposed method, called Cycle Consistent Twin EBMs (CCT-EBM), employs a pair of EBMs in the latent space of an Auto-Encoder to ensure translation symmetry and coupling between domains.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Yutong Xie, Jianpeng Zhang, Lingqiao Liu, Hu Wang, Yiwen Ye, Johan Verjans, Yong Xia
Summary: This paper proposes a hybrid pre-training paradigm that combines self-supervised learning and supervised learning to improve the representation quality for medical image segmentation tasks. It introduces a reference task in self-supervised learning and optimizes the model using a gradient matching method. The experimental results demonstrate the effectiveness of this approach on multiple medical image segmentation benchmarks.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye
Summary: The increasing demand for AI systems to monitor human errors and abnormalities in healthcare presents challenges. This study presents a model called Medical X-VL, which is tailored for the medical domain and outperformed current state-of-the-art models in two medical image datasets. The model enables various zero-shot tasks for monitoring AI in the medical domain.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, Neda Davoudi, Xose Luis Dean-Ben, Fernando Perez-Cruz, Daniel Razansky
Summary: Optoacoustic imaging is a technique that uses optical excitation and ultrasound detection for biological tissue imaging. The quality of the images depends on the extent of tomographic coverage provided by the ultrasound detector arrays. However, full coverage is not always possible due to experimental constraints. The proposed signal domain adaptation network aims to reduce limited-view artifacts in the images.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
Summary: In this work, a novel framework called SynCLay is proposed for automated synthesis of histology images based on user-defined cellular layouts. The framework can generate realistic and high-quality histology images with different cellular arrangements, which is helpful for studying the role of cells in the tumor microenvironment. The framework integrates a nuclear segmentation and classification model to refine nuclear structures and generate nuclear masks. Evaluation using quantitative metrics and feedback from pathologists shows that the synthetic images generated by SynCLay have high realism scores and can accurately differentiate between benign and malignant tumors.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
Summary: Survival analysis is a valuable tool in healthcare for predicting the time to specific events. This paper introduces CenTime, a novel approach that directly estimates the time to event. The method performs well with censored data and can be easily integrated with deep learning models. Compared to standard methods, CenTime offers superior performance in predicting event time while maintaining comparable ranking performance.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed
Summary: Most segmentation losses, such as CE and Dice, are variants of the Cross-Entropy or Dice losses. This work provides a theoretical analysis that shows a deeper connection between CE and Dice than previously thought. From a constrained-optimization perspective, both CE and Dice decompose into similar ground-truth matching terms and region-size penalty terms. The analysis uncovers hidden region-size biases: Dice has an intrinsic bias towards extremely imbalanced solutions, while CE implicitly encourages the ground-truth region proportions. Based on this analysis, a principled and simple solution is proposed to explicitly control the region-size bias.
MEDICAL IMAGE ANALYSIS
(2024)