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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
In the context of automatic medical image segmentation based on statistical learning, raters’ variability of ground truth segmentations in training datasets is a widely recognized issue. Indeed, the reference information is provided by experts but bias due to their knowledge may affect the quality of the ground truth data, thus hindering creation of robust and reliable datasets employed in segmentation
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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
Ultrasound is a promising medical imaging modality benefiting from low-cost and real-time acquisition. Accurate tracking of an anatomical landmark has been of high interest for various clinical workflows such as minimally invasive surgery and ultrasound-guided radiation therapy. However, tracking an anatomical landmark accurately in ultrasound video is very challenging, due to landmark deformation
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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
The brain development during the perinatal period is characterized by rapid changes in both structure and function, which have significant impact on the cognitive and behavioral abilities later in life. Accurate assessment of brain age is a crucial indicator for brain development maturity and can help predict the risk of neonatal pathology. However, evaluating neonatal brains using magnetic resonance
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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
Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals,
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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
As computer vision algorithms increase in capability, their applications in clinical systems will become more pervasive. These applications include: diagnostics, such as colonoscopy and bronchoscopy; guiding biopsies, minimally invasive interventions, and surgery; automating instrument motion; and providing image guidance using pre-operative scans. Many of these applications depend on the specific
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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
Batch Normalization’s (BN) unique property of depending on other samples in a batch is known to cause problems in several tasks, including sequence modeling. Yet, BN-related issues are hardly studied for long video understanding, despite the ubiquitous use of BN in CNNs (Convolutional Neural Networks) for feature extraction. Especially in surgical workflow analysis, where the lack of pretrained feature
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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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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
Curation of large, diverse MRI datasets via multi-institutional collaborations can help improve learning of generalizable synthesis models that reliably translate source- onto target-contrast images. To facilitate collaborations, federated learning (FL) adopts decentralized model training while mitigating privacy concerns by avoiding sharing of imaging data. However, conventional FL methods can be
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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
Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer
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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
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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
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Hybrid unsupervised representation learning and pseudo-label supervised self-distillation for rare disease imaging phenotype classification with dispersion-aware imbalance correction Med. Image Anal. (IF 10.9) Pub Date : 2024-02-07 Jinghan Sun, Dong Wei, Liansheng Wang, Yefeng Zheng
Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging phenotype classification of rare diseases is challenging due to the severe shortage of training examples. Few-shot learning (FSL) methods tackle this challenge by extracting generalizable prior knowledge from a large base dataset of common diseases and normal controls and transferring
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GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy Med. Image Anal. (IF 10.9) Pub Date : 2024-02-02 André Ferreira, Jianning Li, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger
With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts
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CF-Loss: Clinically-relevant feature optimised loss function for retinal multi-class vessel segmentation and vascular feature measurement Med. Image Anal. (IF 10.9) Pub Date : 2024-02-02 Yukun Zhou, MouCheng Xu, Yipeng Hu, Stefano B. Blumberg, An Zhao, Siegfried K. Wagner, Pearse A. Keane, Daniel C. Alexander
Characterising clinically-relevant vascular features, such as vessel density and fractal dimension, can benefit biomarker discovery and disease diagnosis for both ophthalmic and systemic diseases. In this work, we explicitly encode vascular features into an end-to-end loss function for multi-class vessel segmentation, categorising pixels into artery, vein, uncertain pixels, and background. This clinically-relevant
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Immune subtyping of melanoma whole slide images using multiple instance learning Med. Image Anal. (IF 10.9) Pub Date : 2024-02-01 Lucy Godson, Navid Alemi, Jérémie Nsengimana, Graham P. Cook, Emily L. Clarke, Darren Treanor, D. Timothy Bishop, Julia Newton-Bishop, Ali Gooya, Derek Magee
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Blurred streamlines: A novel representation to reduce redundancy in tractography Med. Image Anal. (IF 10.9) Pub Date : 2024-02-01 Ilaria Gabusi, Matteo Battocchio, Sara Bosticardo, Simona Schiavi, Alessandro Daducci
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Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial? Med. Image Anal. (IF 10.9) Pub Date : 2024-01-28 Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
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OCTA-500: A retinal dataset for optical coherence tomography angiography study Med. Image Anal. (IF 10.9) Pub Date : 2024-01-28 Mingchao Li, Kun Huang, Qiuzhuo Xu, Jiadong Yang, Yuhan Zhang, Zexuan Ji, Keren Xie, Songtao Yuan, Qinghuai Liu, Qiang Chen
Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view
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Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification Med. Image Anal. (IF 10.9) Pub Date : 2024-01-28 Tae Jun Jang, Hye Sun Yun, Chang Min Hyun, Jong-Eun Kim, Sang-Hwy Lee, Jin Keun Seo
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Correspondence attention for facial appearance simulation Med. Image Anal. (IF 10.9) Pub Date : 2024-01-26 Xi Fang, Daeseung Kim, Xuanang Xu, Tianshu Kuang, Nathan Lampen, Jungwook Lee, Hannah H. Deng, Michael A.K. Liebschner, James J. Xia, Jaime Gateno, Pingkun Yan
In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative
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Separated collaborative learning for semi-supervised prostate segmentation with multi-site heterogeneous unlabeled MRI data Med. Image Anal. (IF 10.9) Pub Date : 2024-01-26 Zhe Xu, Donghuan Lu, Jie Luo, Yefeng Zheng, Raymond Kai-yu Tong
Segmenting prostate from magnetic resonance imaging (MRI) is a critical procedure in prostate cancer staging and treatment planning. Considering the nature of labeled data scarcity for medical images, semi-supervised learning (SSL) becomes an appealing solution since it can simultaneously exploit limited labeled data and a large amount of unlabeled data. However, SSL relies on the assumption that the
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Neural deformation fields for template-based reconstruction of cortical surfaces from MRI Med. Image Anal. (IF 10.9) Pub Date : 2024-01-26 Fabian Bongratz, Anne-Marie Rickmann, Christian Wachinger
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A universal biventricular coordinate system incorporating valve annuli: Validation in congenital heart disease Med. Image Anal. (IF 10.9) Pub Date : 2024-01-19 Lisa R Pankewitz, Kristian G Hustad, Sachin Govil, James C Perry, Sanjeet Hegde, Renxiang Tang, Jeffrey H Omens, Alistair A Young, Andrew D McCulloch, Hermenegild J Arevalo
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Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data Med. Image Anal. (IF 10.9) Pub Date : 2024-01-17 Michele Svanera, Mattia Savardi, Alberto Signoroni, Sergio Benini, Lars Muckli
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A transferable in-silico augmented ischemic model for virtual myocardial perfusion imaging and myocardial infarction detection Med. Image Anal. (IF 10.9) Pub Date : 2024-01-14 Zeus Harnod, Chen Lin, Hui-Wen Yang, Zih-Wen Wang, Han-Luen Huang, Tse-Yu Lin, Chun-Yao Huang, Lian-Yu Lin, Hsu-Wen V. Young, Men-Tzung Lo
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Diffusion models for out-of-distribution detection in digital pathology Med. Image Anal. (IF 10.9) Pub Date : 2024-01-13 Jasper Linmans, Gabriel Raya, Jeroen van der Laak, Geert Litjens
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Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative Med. Image Anal. (IF 10.9) Pub Date : 2024-01-13 Boyeong Woo, Craig Engstrom, William Baresic, Jurgen Fripp, Stuart Crozier, Shekhar S. Chandra
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What matters in reinforcement learning for tractography Med. Image Anal. (IF 10.9) Pub Date : 2024-01-11 Antoine Théberge, Christian Desrosiers, Arnaud Boré, Maxime Descoteaux, Pierre-Marc Jodoin
Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually curated reference streamlines. While the performances reported were competitive, the proposed framework is complex, and little is still known about the role and impact of its multiple parts. In this work, we thoroughly explore
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GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification Med. Image Anal. (IF 10.9) Pub Date : 2024-01-06 Dwarikanath Mahapatra, Behzad Bozorgtabar, Zongyuan Ge, Mauricio Reyes
Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the presence of limited labeled data. Another effective technique to enlarge datasets in a small labeled data regime is data augmentation. An intuitive active
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Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer Med. Image Anal. (IF 10.9) Pub Date : 2024-01-05 Neda Zamanitajeddin, Mostafa Jahanifar, Mohsin Bilal, Mark Eastwood, Nasir Rajpoot
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Turning brain MRI into diagnostic PET: 15O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder–decoder networks Med. Image Anal. (IF 10.9) Pub Date : 2023-12-29 Ramy Hussein, David Shin, Moss Y. Zhao, Jia Guo, Guido Davidzon, Gary Steinberg, Michael Moseley, Greg Zaharchuk
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (O-water) is the gold-standard for the measurement of CBF in humans, however, it is not widely available due to its prohibitive costs and the use of short-lived radiopharmaceutical tracers that require
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TestFit: A plug-and-play one-pass test time method for medical image segmentation Med. Image Anal. (IF 10.9) Pub Date : 2023-12-26 Yizhe Zhang, Tao Zhou, Yuhui Tao, Shuo Wang, Ye Wu, Benyuan Liu, Pengfei Gu, Qiang Chen, Danny Z. Chen
Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning
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Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning Med. Image Anal. (IF 10.9) Pub Date : 2023-12-28 Saarthak Kapse, Srijan Das, Jingwei Zhang, Rajarsi R. Gupta, Joel Saltz, Dimitris Samaras, Prateek Prasanna
We propose , a iversity-nducing epresentation earning technique for histopathology imaging. Self-supervised learning (SSL) techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective representations of digitized tissue samples with limited pathologist supervision. Our analysis of vanilla SSL-pretrained models’ attention distribution reveals an insightful
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Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond Med. Image Anal. (IF 10.9) Pub Date : 2023-12-27 Niharika S. D‘Souza, Hongzhi Wang, Andrea Giovannini, Antonio Foncubierta-Rodriguez, Kristen L. Beck, Orest Boyko, Tanveer F. Syeda-Mahmood
With the emergence of multimodal electronic health records, the evidence for diseases, events, or findings may be present across multiple modalities ranging from clinical to imaging and genomic data. Developing effective patient-tailored therapeutic guidance and outcome prediction will require fusing evidence across these modalities. Developing general-purpose frameworks capable of modeling fine-grained
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Adaptive smoothing of retinotopic maps based on Teichmüller parametrization Med. Image Anal. (IF 10.9) Pub Date : 2023-12-26 Yanshuai Tu, Xin Li, Zhong-Lin Lu, Yalin Wang
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Space-feature measures on meshes for mapping spatial transcriptomics Med. Image Anal. (IF 10.9) Pub Date : 2023-12-23 Michael I. Miller, Alain Trouvé, Laurent Younes
Advances in the development of largely automated microscopy methods such as MERFISH for imaging cellular structures in mouse brains are providing spatial detection of micron resolution gene expression. While there has been tremendous progress made in the field of Computational Anatomy (CA) to perform diffeomorphic mapping technologies at the tissue scales for advanced neuroinformatic studies in common
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Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data Med. Image Anal. (IF 10.9) Pub Date : 2023-12-23 Sophie Loizillon, Simona Bottani, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos, Alzheimer’s Disease Neuroimaging Initiative, APPRIMAGE Study Group
Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted
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Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings Med. Image Anal. (IF 10.9) Pub Date : 2023-12-20 Sophia Bano, Alessandro Casella, Francisco Vasconcelos, Abdul Qayyum, Abdesslam Benzinou, Moona Mazher, Fabrice Meriaudeau, Chiara Lena, Ilaria Anita Cintorrino, Gaia Romana De Paolis, Jessica Biagioli, Daria Grechishnikova, Jing Jiao, Bizhe Bai, Yanyan Qiao, Binod Bhattarai, Rebati Raman Gaire, Ronast Subedi, Eduard Vazquez, Szymon Płotka, Danail Stoyanov
Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon’s side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to
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Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development Med. Image Anal. (IF 10.9) Pub Date : 2023-12-21 Igor Zingman, Birgit Stierstorfer, Charlotte Lempp, Fabian Heinemann
We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on healthy data can detect out-of-distribution anomalous samples. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of
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Robust and fast stochastic 4D flow vector-field signature technique for quantifying composite flow dynamics from 4D flow MRI: Application to left atrial flow in atrial fibrillation Med. Image Anal. (IF 10.9) Pub Date : 2023-12-15 Thara Nallamothu, Maurice Pradella, Michael Markl, Philip Greenland, Rod Passman, Mohammed SM Elbaz