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Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification Med. Image Anal. (IF 10.9) Pub Date : 2024-04-17 Qiong Liu, Yu-Jung Tsai, Jean-Dominique Gallezot, Xueqi Guo, Ming-Kai Chen, Darko Pucar, Colin Young, Vladimir Panin, Michael Casey, Tianshun Miao, Huidong Xie, Xiongchao Chen, Bo Zhou, Richard Carson, Chi Liu
The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior
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Active learning using adaptable task-based prioritisation Med. Image Anal. (IF 10.9) Pub Date : 2024-04-16 Shaheer U. Saeed, João Ramalhinho, Mark Pinnock, Ziyi Shen, Yunguan Fu, Nina Montaña-Brown, Ester Bonmati, Dean C. Barratt, Stephen P. Pereira, Brian Davidson, Matthew J. Clarkson, Yipeng Hu
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Stepwise incremental pretraining for integrating discriminative, restorative, and adversarial learning Med. Image Anal. (IF 10.9) Pub Date : 2024-04-16 Zuwei Guo, Nahid Ul Islam, Michael B. Gotway, Jianming Liang
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Unsupervised model adaptation for source-free segmentation of medical images Med. Image Anal. (IF 10.9) Pub Date : 2024-04-14 Serban Stan, Mohammad Rostami
The recent prevalence of deep neural networks has led semantic segmentation networks to achieve human-level performance in the medical field, provided they are given sufficient training data. However, these networks often fail to generalize when tasked with creating semantic maps for out-of-distribution images, necessitating re-training on new distributions. This labor-intensive process requires expert
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A survey of label-noise deep learning for medical image analysis Med. Image Anal. (IF 10.9) Pub Date : 2024-04-12 Jialin Shi, Kailai Zhang, Chenyi Guo, Youquan Yang, Yali Xu, Ji Wu
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A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation Med. Image Anal. (IF 10.9) Pub Date : 2024-04-09 Ming Zhang, Ruimin Feng, Zhenghao Li, Jie Feng, Qing Wu, Zhiyong Zhang, Chengxin Ma, Jinsong Wu, Fuhua Yan, Chunlei Liu, Yuyao Zhang, Hongjiang Wei
Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse problem for QSM reconstruction. However, they require extensive paired training data that are typically unavailable and suffer from generalization problems
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Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval Med. Image Anal. (IF 10.9) Pub Date : 2024-04-09 Dingyi Hu, Zhiguo Jiang, Jun Shi, Fengying Xie, Kun Wu, Kunming Tang, Ming Cao, Jianguo Huai, Yushan Zheng
Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited
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Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning Med. Image Anal. (IF 10.9) Pub Date : 2024-04-06 Di Zhang, Fangrong Zong, Qichen Zhang, Yunhui Yue, Fan Zhang, Kun Zhao, Dawei Wang, Pan Wang, Xi Zhang, Yong Liu
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Domain generalization for retinal vessel segmentation via Hessian-based vector field Med. Image Anal. (IF 10.9) Pub Date : 2024-04-06 Dewei Hu, Hao Li, Han Liu, Ipek Oguz
Blessed by vast amounts of data, learning-based methods have achieved remarkable performance in countless tasks in computer vision and medical image analysis. Although these deep models can simulate highly nonlinear mapping functions, they are not robust with regard to the domain shift of input data. This is a significant concern that impedes the large-scale deployment of deep models in medical images
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Focused active learning for histopathological image classification Med. Image Anal. (IF 10.9) Pub Date : 2024-04-04 Arne Schmidt, Pablo Morales-Álvarez, Lee AD Cooper, Lee A. Newberg, Andinet Enquobahrie, Rafael Molina, Aggelos K. Katsaggelos
Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambiguities, and class imbalances, as commonly seen in the medical field. The lack of precise uncertainty estimations leads to the acquisition of images with
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Spatial attention-based implicit neural representation for arbitrary reduction of MRI slice spacing Med. Image Anal. (IF 10.9) Pub Date : 2024-03-30 Xin Wang, Sheng Wang, Honglin Xiong, Kai Xuan, Zixu Zhuang, Mengjun Liu, Zhenrong Shen, Xiangyu Zhao, Lichi Zhang, Qian Wang
Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution. Super-resolution technique can enhance the through-plane resolution of MR images to facilitate downstream visualization and computer-aided diagnosis. However, most existing works train the super-resolution network at
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Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies Med. Image Anal. (IF 10.9) Pub Date : 2024-03-29 Tim J.M. Jaspers, Tim G.W. Boers, Carolus H.J. Kusters, Martijn R. Jong, Jelmer B. Jukema, Albert J. de Groof, Jacques J. Bergman, Peter H.N. de With, Fons van der Sommen
Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination
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Keyhole-aware laparoscopic augmented reality Med. Image Anal. (IF 10.9) Pub Date : 2024-03-28 Yamid Espinel, Navid Rabbani, Thien Bao Bui, Mathieu Ribeiro, Emmanuel Buc, Adrien Bartoli
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Boundary-aware information maximization for self-supervised medical image segmentation Med. Image Anal. (IF 10.9) Pub Date : 2024-03-28 Jizong Peng, Ping Wang, Marco Pedersoli, Christian Desrosiers
Self-supervised representation learning can boost the performance of a pre-trained network on downstream tasks for which labeled data is limited. A popular method based on this paradigm, known as contrastive learning, works by constructing sets of positive and negative pairs from the data, and then pulling closer the representations of positive pairs while pushing apart those of negative pairs. Although
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Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images Med. Image Anal. (IF 10.9) Pub Date : 2024-03-28 Martin J. Hetz, Tabea-Clara Bucher, Titus J. Brinker
The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream tasks like cancer diagnosis. Furthermore, different stainings lead to biases in
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DermSynth3D: Synthesis of in-the-wild annotated dermatology images Med. Image Anal. (IF 10.9) Pub Date : 2024-03-26 Ashish Sinha, Jeremy Kawahara, Arezou Pakzad, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, Ghassan Hamarneh
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Sensorless volumetric reconstruction of fetal brain freehand ultrasound scans with deep implicit representation Med. Image Anal. (IF 10.9) Pub Date : 2024-03-26 Pak-Hei Yeung, Linde S. Hesse, Moska Aliasi, Monique C. Haak, INTERGROWTH-21st Consortium, Weidi Xie, Ana I.L. Namburete
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Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets Med. Image Anal. (IF 10.9) Pub Date : 2024-03-25 Soopil Kim, Heejung Park, Myeongkyun Kang, Kyong Hwan Jin, Ehsan Adeli, Kilian M. Pohl, Sang Hyun Park
The state-of-the-art multi-organ CT segmentation relies on deep learning models, which only generalize when trained on large samples of carefully curated data. However, it is challenging to train a single model that can segment all organs and types of tumors since most large datasets are partially labeled or are acquired across multiple institutes that may differ in their acquisitions. A possible solution
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Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks Med. Image Anal. (IF 10.9) Pub Date : 2024-03-25 Yang Gao, Zhuang Xiong, Shanshan Shan, Yin Liu, Pengfei Rong, Min Li, Alan H. Wilman, G. Bruce Pike, Feng Liu, Hongfu Sun
Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations
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Diabetic foot ulcers segmentation challenge report: Benchmark and analysis Med. Image Anal. (IF 10.9) Pub Date : 2024-03-24 Moi Hoon Yap, Bill Cassidy, Michal Byra, Ting-yu Liao, Huahui Yi, Adrian Galdran, Yung-Han Chen, Raphael Brüngel, Sven Koitka, Christoph M. Friedrich, Yu-wen Lo, Ching-hui Yang, Kang Li, Qicheng Lao, Miguel A. González Ballester, Gustavo Carneiro, Yi-Jen Ju, Juinn-Dar Huang, Joseph M. Pappachan, Neil D. Reeves, Vishnu Chandrabalan, Darren Dancey, Connah Kendrick
Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning
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A causality-inspired generalized model for automated pancreatic cancer diagnosis Med. Image Anal. (IF 10.9) Pub Date : 2024-03-22 Jiaqi Qu, Xiang Xiao, Xunbin Wei, Xiaohua Qian
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Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge Med. Image Anal. (IF 10.9) Pub Date : 2024-03-22 Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan, Robert Klopfleisch, Jonas Ammeling, Jonathan Ganz, Frauke Wilm, Mitko Veta, Samir Jabari, Markus Eckstein, Jonas Annuscheit, Christian Krumnow, Engin Bozaba, Sercan Çayır, Hongyan Gu, Xiang ‘Anthony’ Chen, Mostafa Jahanifar, Adam Shephard, Satoshi Kondo, Satoshi Kasai, Sujatha Kotte, V.G. Saipradeep, Maxime W. Lafarge, Viktor H. Koelzer, Ziyue
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Automatic multi-view pose estimation in focused cardiac ultrasound Med. Image Anal. (IF 10.9) Pub Date : 2024-03-22 João Freitas, João Gomes-Fonseca, Ana Claudia Tonelli, Jorge Correia-Pinto, Jaime C. Fonseca, Sandro Queirós
Focused cardiac ultrasound (FoCUS) is a valuable point-of-care method for evaluating cardiovascular structures and function, but its scope is limited by equipment and operator’s experience, resulting in primarily qualitative 2D exams. This study presents a novel framework to automatically estimate the 3D spatial relationship between standard FoCUS views. The proposed framework uses a multi-view U-Net-like
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A model-based MR parameter mapping network robust to substantial variations in acquisition settings Med. Image Anal. (IF 10.9) Pub Date : 2024-03-21 Qiqi Lu, Jialong Li, Zifeng Lian, Xinyuan Zhang, Qianjin Feng, Wufan Chen, Jianhua Ma, Yanqiu Feng
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Classification, registration and segmentation of ear canal impressions using convolutional neural networks Med. Image Anal. (IF 10.9) Pub Date : 2024-03-21 Stylianos Dritsas, Kenneth Wei De Chua, Zhi Hwee Goh, Robert E. Simpson
Today, fitting bespoke hearing aids involves injecting silicone into patients’ ears to produce ear canal molds. These are subsequently 3D scanned to create digital ear canal impressions. However, before digital impressions can be used they require a substantial amount of effort in manual 3D editing. In this article, we present computational methods to pre-process ear canal impressions. The aim is to
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Self-supervised learning for medical image data with anatomy-oriented imaging planes Med. Image Anal. (IF 10.9) Pub Date : 2024-03-21 Tianwei Zhang, Dong Wei, Mengmeng Zhu, Shi Gu, Yefeng Zheng
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Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis Med. Image Anal. (IF 10.9) Pub Date : 2024-03-19 Wei Wang, Li Xiao, Gang Qu, Vince D. Calhoun, Yu-Ping Wang, Xiaoyan Sun
Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this
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CellViT: Vision Transformers for precise cell segmentation and classification Med. Image Anal. (IF 10.9) Pub Date : 2024-03-16 Fabian Hörst, Moritz Rempe, Lukas Heine, Constantin Seibold, Julius Keyl, Giulia Baldini, Selma Ugurel, Jens Siveke, Barbara Grünwald, Jan Egger, Jens Kleesiek
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STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution Med. Image Anal. (IF 10.9) Pub Date : 2024-03-12 Jun Lyu, Shuo Wang, Yapeng Tian, Jing Zou, Shunjie Dong, Chengyan Wang, Angelica I. Aviles-Rivero, Jing Qin
Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively
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Use of superpixels for improvement of inter-rater and intra-rater reliability during annotation of medical images Med. Image Anal. (IF 10.9) Pub Date : 2024-03-12 Daniel Gut, Marco Trombini, Iwona Kucybała, Kamil Krupa, Miłosz Rozynek, Silvana Dellepiane, Zbisław Tabor, Wadim Wojciechowski
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Anomaly-guided weakly supervised lesion segmentation on retinal OCT images Med. Image Anal. (IF 10.9) Pub Date : 2024-03-12 Jiaqi Yang, Nitish Mehta, Gozde Demirci, Xiaoling Hu, Meera S. Ramakrishnan, Mina Naguib, Chao Chen, Chia-Ling Tsai
The availability of big data can transform the studies in biomedical research to generate greater scientific insights if expert labeling is available to facilitate supervised learning. However, data annotation can be labor-intensive and cost-prohibitive if pixel-level precision is required. Weakly supervised semantic segmentation (WSSS) with image-level labeling has emerged as a promising solution
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Constructing hierarchical attentive functional brain networks for early AD diagnosis Med. Image Anal. (IF 10.9) Pub Date : 2024-03-11 Jianjia Zhang, Yunan Guo, Luping Zhou, Lei Wang, Weiwen Wu, Dinggang Shen
Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing
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Long-short diffeomorphism memory network for weakly-supervised ultrasound landmark tracking Med. Image Anal. (IF 10.9) Pub Date : 2024-03-11 Zhihua Liu, Bin Yang, Yan Shen, Xuejun Ni, Sotirios A. Tsaftaris, Huiyu Zhou
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Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis Med. Image Anal. (IF 10.9) Pub Date : 2024-03-07 Haiyan Zhao, Hongjie Cai, Manhua Liu
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Task sub-type states decoding via group deep bidirectional recurrent neural network Med. Image Anal. (IF 10.9) Pub Date : 2024-03-06 Shijie Zhao, Long Fang, Yang Yang, Guochang Tang, Guoxin Luo, Junwei Han, Tianming Liu, Xintao Hu
Decoding brain states under different cognitive tasks from functional magnetic resonance imaging (fMRI) data has attracted great attention in the neuroimaging filed. However, the well-known temporal dependency in fMRI sequences has not been fully exploited in existing studies, due to the limited temporal-modeling capacity of the backbone machine learning algorithms and rigid training sample organization
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Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI Med. Image Anal. (IF 10.9) Pub Date : 2024-03-06 Lintao Zhang, Lihong Wang, Minhui Yu, Rong Wu, David C. Steffens, Guy G. Potter, Mingxia Liu
Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer’s disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression
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Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia Med. Image Anal. (IF 10.9) Pub Date : 2024-03-06 Zhengwang Xia, Tao Zhou, Saqib Mamoon, Jianfeng Lu
Brain functional network analysis has become a popular method to explore the laws of brain organization and identify biomarkers of neurological diseases. However, it is still a challenging task to construct an ideal brain network due to the limited understanding of the human brain. Existing methods often ignore the impact of temporal-lag on the results of brain network modeling, which may lead to some
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Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning Med. Image Anal. (IF 10.9) Pub Date : 2024-03-05 Álvaro Planchuelo-Gómez, Maxime Descoteaux, Hugo Larochelle, Jana Hutter, Derek K. Jones, Chantal M.W. Tax
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Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures Med. Image Anal. (IF 10.9) Pub Date : 2024-03-02 Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin, Simon Graham, Shan E. Ahmed Raza, Fayyaz Minhas, Nasir Rajpoot
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algorithms have been proposed but they are sensitive
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Tracking and mapping in medical computer vision: A review Med. Image Anal. (IF 10.9) Pub Date : 2024-03-02 Adam Schmidt, Omid Mohareri, Simon DiMaio, Michael C. Yip, Septimiu E. Salcudean
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TransVFS: A spatio-temporal local–global transformer for vision-based force sensing during ultrasound-guided prostate biopsy Med. Image Anal. (IF 10.9) Pub Date : 2024-03-02 Yibo Wang, Zhichao Ye, Mingwei Wen, Huageng Liang, Xuming Zhang
Robot-assisted prostate biopsy is a new technology to diagnose prostate cancer, but its safety is influenced by the inability of robots to sense the tool-tissue interaction force accurately during biopsy. Recently, vision based force sensing (VFS) provides a potential solution to this issue by utilizing image sequences to infer the interaction force. However, the existing mainstream VFS methods cannot
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On the pitfalls of Batch Normalization for end-to-end video learning: A study on surgical workflow analysis Med. Image Anal. (IF 10.9) Pub Date : 2024-03-01 Dominik Rivoir, Isabel Funke, Stefanie Speidel
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Privacy preserving image registration Med. Image Anal. (IF 10.9) Pub Date : 2024-03-01 Riccardo Taiello, Melek Önen, Francesco Capano, Olivier Humbert, Marco Lorenzi
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical
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RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps Med. Image Anal. (IF 10.9) Pub Date : 2024-02-29 Min Shi, Yu Tian, Yan Luo, Tobias Elze, Mengyu Wang
Optical coherence tomography imaging provides a crucial clinical measurement for diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer (RNFL) thickness (RNFLT) map. Researchers have been increasingly using neural models to extract meaningful features from the RNFLT map, aiming to identify biomarkers for glaucoma and its progression. However, accurately representing
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CLANet: A comprehensive framework for cross-batch cell line identification using brightfield images Med. Image Anal. (IF 10.9) Pub Date : 2024-02-29 Lei Tong, Adam Corrigan, Navin Rathna Kumar, Kerry Hallbrook, Jonathan Orme, Yinhai Wang, Huiyu Zhou
Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated
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Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials Med. Image Anal. (IF 10.9) Pub Date : 2024-02-28 Julia Camps, Lucas Arantes Berg, Zhinuo Jenny Wang, Rafael Sebastian, Leto Luana Riebel, Ruben Doste, Xin Zhou, Rafael Sachetto, James Coleman, Brodie Lawson, Vicente Grau, Kevin Burrage, Alfonso Bueno-Orovio, Rodrigo Weber dos Santos, Blanca Rodriguez
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Expectation maximisation pseudo labels Med. Image Anal. (IF 10.9) Pub Date : 2024-02-27 Moucheng Xu, Yukun Zhou, Chen Jin, Marius de Groot, Daniel C. Alexander, Neil P. Oxtoby, Yipeng Hu, Joseph Jacob
In this paper, we study pseudo-labelling. Pseudo-labelling employs raw inferences on unlabelled data as pseudo-labels for self-training. We elucidate the empirical successes of pseudo-labelling by establishing a link between this technique and the Expectation Maximisation algorithm. Through this, we realise that the original pseudo-labelling serves as an empirical estimation of its more comprehensive
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Cross-scale multi-instance learning for pathological image diagnosis Med. Image Anal. (IF 10.9) Pub Date : 2024-02-27 Ruining Deng, Can Cui, Lucas W. Remedios, Shunxing Bao, R. Michael Womick, Sophie Chiron, Jia Li, Joseph T. Roland, Ken S. Lau, Qi Liu, Keith T. Wilson, Yaohong Wang, Lori A. Coburn, Bennett A. Landman, Yuankai Huo
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects ( sets of smaller image patches). However, such processing is typically performed at a single scale (, 20 magnification) of WSIs
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One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis Med. Image Anal. (IF 10.9) Pub Date : 2024-02-23 Onat Dalmaz, Muhammad U. Mirza, Gokberk Elmas, Muzaffer Ozbey, Salman U.H. Dar, Emir Ceyani, Kader K. Oguz, Salman Avestimehr, Tolga Çukur
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SUGAR: Spherical ultrafast graph attention framework for cortical surface registration Med. Image Anal. (IF 10.9) Pub Date : 2024-02-23 Jianxun Ren, Ning An, Youjia Zhang, Danyang Wang, Zhenyu Sun, Cong Lin, Weigang Cui, Weiwei Wang, Ying Zhou, Wei Zhang, Qingyu Hu, Ping Zhang, Dan Hu, Danhong Wang, Hesheng Liu
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TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance Med. Image Anal. (IF 10.9) Pub Date : 2024-02-23 Yuqian Chen, Leo R. Zekelman, Chaoyi Zhang, Tengfei Xue, Yang Song, Nikos Makris, Yogesh Rathi, Alexandra J. Golby, Weidong Cai, Fan Zhang, Lauren J. O'Donnell
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Semi-supervised medical image classification via distance correlation minimization and graph attention regularization Med. Image Anal. (IF 10.9) Pub Date : 2024-02-21 Abel Díaz Berenguer, Maryna Kvasnytsia, Matías Nicolás Bossa, Tanmoy Mukherjee, Nikos Deligiannis, Hichem Sahli
We propose a novel semi-supervised learning method to leverage unlabeled data alongside minimal annotated data and improve medical imaging classification performance in realistic scenarios with limited labeling budgets to afford data annotations. Our method introduces distance correlation to minimize correlations between feature representations from different views of the same image encoded with non-coupled
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Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation Med. Image Anal. (IF 10.9) Pub Date : 2024-02-21 Zhanshi Zhu, Xinghua Ma, Wei Wang, Suyu Dong, Kuanquan Wang, Lianming Wu, Gongning Luo, Guohua Wang, Shuo Li
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Mutual learning with reliable pseudo label for semi-supervised medical image segmentation Med. Image Anal. (IF 10.9) Pub Date : 2024-02-21 Jiawei Su, Zhiming Luo, Sheng Lian, Dazhen Lin, Shaozi Li
Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can alleviate the burden of densely annotated data. Substantial advancements have been achieved by integrating consistency-regularization and pseudo-labeling techniques. The quality of the pseudo-labels
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LESS: Label-efficient multi-scale learning for cytological whole slide image screening Med. Image Anal. (IF 10.9) Pub Date : 2024-02-20 Beidi Zhao, Wenlong Deng, Zi Han (Henry) Li, Chen Zhou, Zuhua Gao, Gang Wang, Xiaoxiao Li
In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency
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DOVE: Doodled vessel enhancement for photoacoustic angiography super resolution Med. Image Anal. (IF 10.9) Pub Date : 2024-02-13 Yuanzheng Ma, Wangting Zhou, Rui Ma, Erqi Wang, Sihua Yang, Yansong Tang, Xiao-Ping Zhang, Xun Guan
Deep-learning-based super-resolution photoacoustic angiography (PAA) has emerged as a valuable tool for enhancing the resolution of blood vessel images and aiding in disease diagnosis. However, due to the scarcity of training samples, PAA super-resolution models do not generalize well, especially in the challenging in-vivo imaging of organs with deep tissue penetration. Furthermore, prolonged exposure
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Neural orientation distribution fields for estimation and uncertainty quantification in diffusion MRI Med. Image Anal. (IF 10.9) Pub Date : 2024-02-09 William Consagra, Lipeng Ning, Yogesh Rathi
Inferring brain connectivity and structure requires accurate estimation of the orientation distribution function (ODF), which encodes key local tissue properties. However, estimating the ODF from diffusion MRI (dMRI) signals is a challenging inverse problem due to obstacles such as significant noise, high-dimensional parameter spaces, and sparse angular measurements. In this paper, we address these
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Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection Med. Image Anal. (IF 10.9) Pub Date : 2024-02-08 Philipp Seeböck, José Ignacio Orlando, Martin Michl, Julia Mai, Ursula Schmidt-Erfurth, Hrvoje Bogunović
Automated lesion detection in retinal optical coherence tomography (OCT) scans has shown promise for several clinical applications, including diagnosis, monitoring and guidance of treatment decisions. However, segmentation models still struggle to achieve the desired results for some complex lesions or datasets that commonly occur in real-world, e.g. due to variability of lesion phenotypes, image quality
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Learning continuous shape priors from sparse data with neural implicit functions Med. Image Anal. (IF 10.9) Pub Date : 2024-02-08 Tamaz Amiranashvili, David Lüdke, Hongwei Bran Li, Stefan Zachow, Bjoern H. Menze
Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g
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Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data Med. Image Anal. (IF 10.9) Pub Date : 2024-02-08 Chuan-Xian Ren, Geng-Xin Xu, Dao-Qing Dai, Li Lin, Ying Sun, Qing-Shan Liu
Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However