MLCN@MICCAI Pub Date : 2023-08-29 DOI: 10.48550/arXiv.2308.15564

Jiyao Wang, N. Dvornek, L. Staib, J. Duncan

引用次数: 0

VesselShot: Few-shot learning for cerebral blood vessel segmentation VesselShot:脑血管分割的少射学习

MLCN@MICCAI Pub Date : 2023-08-28 DOI: 10.48550/arXiv.2308.14626

M. Aktar, H. Rivaz, Marta Kersten-Oertel, Yiming Xiao

引用次数: 0

A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging 一种用于磁共振成像超分辨率的三播放器GAN

MLCN@MICCAI Pub Date : 2023-03-24 DOI: 10.48550/arXiv.2303.13900

Qi Wang, Lucas Mahler, Julius Steiglechner, Florian Birk, K. Scheffler, G. Lohmann

{"title":"A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging","authors":"Qi Wang, Lucas Mahler, Julius Steiglechner, Florian Birk, K. Scheffler, G. Lohmann","doi":"10.48550/arXiv.2303.13900","DOIUrl":"https://doi.org/10.48550/arXiv.2303.13900","url":null,"abstract":"Learning based single image super resolution (SISR) task is well investigated in 2D images. However, SISR for 3D Magnetics Resonance Images (MRI) is more challenging compared to 2D, mainly due to the increased number of neural network parameters, the larger memory requirement and the limited amount of available training data. Current SISR methods for 3D volumetric images are based on Generative Adversarial Networks (GANs), especially Wasserstein GANs due to their training stability. Other common architectures in the 2D domain, e.g. transformer models, require large amounts of training data and are therefore not suitable for the limited 3D data. However, Wasserstein GANs can be problematic because they may not converge to a global optimum and thus produce blurry results. Here, we propose a new method for 3D SR based on the GAN framework. Specifically, we use instance noise to balance the GAN training. Furthermore, we use a relativistic GAN loss function and an updating feature extractor during the training process. We show that our method produces highly accurate results. We also show that we need very few training samples. In particular, we need less than 30 samples instead of thousands of training samples that are typically required in previous studies. Finally, we show improved out-of-sample results produced by our model.","PeriodicalId":344785,"journal":{"name":"MLCN@MICCAI","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning? 基于人际神经同步性的自闭症谱系障碍分类:使用无监督图表示学习的二元神经生物标记可以改进分类吗?

MLCN@MICCAI Pub Date : 2022-08-17 DOI: 10.48550/arXiv.2208.08902

C. Gerloff, K. Konrad, Jana A. Kruppa, M. Schulte-Rüther, Vanessa Reindl

{"title":"Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?","authors":"C. Gerloff, K. Konrad, Jana A. Kruppa, M. Schulte-Rüther, Vanessa Reindl","doi":"10.48550/arXiv.2208.08902","DOIUrl":"https://doi.org/10.48550/arXiv.2208.08902","url":null,"abstract":". Research in machine learning for autism spectrum disorder (ASD) classiﬁcation bears the promise to improve clinical diagnoses. However, recent studies in clinical imaging have shown the limited generalization of biomarkers across and beyond benchmark datasets. Despite increasing model complexity and sample size in neuroimaging, the classiﬁcation performance of ASD remains far away from clinical application. This raises the question of how we can overcome these barriers to develop early biomarkers for ASD. One approach might be to rethink how we operationalize the theoretical basis of this disease in machine learning models. Here we introduced unsupervised graph representations that explicitly map the neural mechanisms of a core aspect of ASD, deﬁcits in dyadic social interaction, as assessed by dual brain record-ings, termed hyperscanning, and evaluated their predictive performance. The proposed method diﬀers from existing approaches in that it is more suitable to capture social interaction deﬁcits on a neural level and is applicable to young children and infants. First results from functional near-infrared spectroscopy data indicate potential predictive capacities of a task-agnostic, interpretable graph representation. This ﬁrst eﬀort to leverage interaction-related deﬁcits on neural level to classify ASD may stimulate new approaches and methods to enhance existing models to achieve developmental ASD biomarkers in the future.","PeriodicalId":344785,"journal":{"name":"MLCN@MICCAI","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128823787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Study of Demographic Bias in CNN-based Brain MR Segmentation 基于cnn的脑MR分割中人口统计学偏差的研究

MLCN@MICCAI Pub Date : 2022-08-13 DOI: 10.48550/arXiv.2208.06613

Stefanos Ioannou, Hana Chockler, A. Hammers, A. King

{"title":"A Study of Demographic Bias in CNN-based Brain MR Segmentation","authors":"Stefanos Ioannou, Hana Chockler, A. Hammers, A. King","doi":"10.48550/arXiv.2208.06613","DOIUrl":"https://doi.org/10.48550/arXiv.2208.06613","url":null,"abstract":". Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whether CNN models for brain MR segmentation have the potential to contain sex or race bias when trained with imbalanced training sets. We train multiple instances of the FastSurferCNN model using diﬀerent levels of sex imbalance in white subjects. We evaluate the performance of these models separately for white male and white female test sets to assess sex bias, and furthermore evaluate them on black male and black female test sets to assess potential racial bias. We ﬁnd signif-icant sex and race bias eﬀects in segmentation model performance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more signiﬁcant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for CNN-based brain MR segmentation, to avoid maintaining or even exacerbating existing health inequalities through biased research study ﬁndings.","PeriodicalId":344785,"journal":{"name":"MLCN@MICCAI","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122580475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma 前庭神经鞘瘤金属内外分割的边界距离损失

MLCN@MICCAI Pub Date : 2022-08-09 DOI: 10.1007/978-3-031-17899-3_8

Navodini Wijethilake, A. Kujawa, R. Dorent, M. Asad, Anna Oviedova, T. Vercauteren, J. Shapey

引用次数: 5

Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury 自动病变分析提高外伤性脑损伤预后预测效率

MLCN@MICCAI Pub Date : 2022-08-08 DOI: 10.48550/arXiv.2208.04114

Margherita Rosnati, E. Soreq, M. Monteiro, Lucia M. Li, Neil S N Graham, K. Zimmerman, C. Rossi, G. Carrara, G. Bertolini, D. Sharp, B. Glocker

{"title":"Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury","authors":"Margherita Rosnati, E. Soreq, M. Monteiro, Lucia M. Li, Neil S N Graham, K. Zimmerman, C. Rossi, G. Carrara, G. Bertolini, D. Sharp, B. Glocker","doi":"10.48550/arXiv.2208.04114","DOIUrl":"https://doi.org/10.48550/arXiv.2208.04114","url":null,"abstract":"The accurate prognosis for traumatic brain injury (TBI) patients is difficult yet essential to inform therapy, patient management, and long-term after-care. Patient characteristics such as age, motor and pupil responsiveness, hypoxia and hypotension, and radiological findings on computed tomography (CT), have been identified as important variables for TBI outcome prediction. CT is the acute imaging modality of choice in clinical practice because of its acquisition speed and widespread availability. However, this modality is mainly used for qualitative and semi-quantitative assessment, such as the Marshall scoring system, which is prone to subjectivity and human errors. This work explores the predictive power of imaging biomarkers extracted from routinely-acquired hospital admission CT scans using a state-of-the-art, deep learning TBI lesion segmentation method. We use lesion volumes and corresponding lesion statistics as inputs for an extended TBI outcome prediction model. We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers. We find that automatically extracted quantitative CT features perform similarly or better than the Marshall score in predicting unfavourable TBI outcomes. Leveraging automatic atlas alignment, we also identify frontal extra-axial lesions as important indicators of poor outcome. Our work may contribute to a better understanding of TBI, and provides new insights into how automated neuroimaging analysis can be used to improve prognostication after TBI.","PeriodicalId":344785,"journal":{"name":"MLCN@MICCAI","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125547559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

fMRI-S4: learning short- and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models fMRI- s4:学习短期和长期动态fMRI依赖使用1D卷积和状态空间模型

MLCN@MICCAI Pub Date : 2022-08-08 DOI: 10.48550/arXiv.2208.04166

A. E. Gazzar, R. Thomas, G. Wingen

{"title":"fMRI-S4: learning short- and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models","authors":"A. E. Gazzar, R. Thomas, G. Wingen","doi":"10.48550/arXiv.2208.04166","DOIUrl":"https://doi.org/10.48550/arXiv.2208.04166","url":null,"abstract":"Single-subject mapping of resting-state brain functional activity to non-imaging phenotypes is a major goal of neuroimaging. The large majority of learning approaches applied today rely either on static representations or on short-term temporal correlations. This is at odds with the nature of brain activity which is dynamic and exhibit both short- and long-range dependencies. Further, new sophisticated deep learning approaches have been developed and validated on single tasks/datasets. The application of these models for the study of a different targets typically require exhaustive hyperparameter search, model engineering and trial and error to obtain competitive results with simpler linear models. This in turn limit their adoption and hinder fair benchmarking in a rapidly developing area of research. To this end, we propose fMRI-S4; a versatile deep learning model for the classification of phenotypes and psychiatric disorders from the timecourses of resting-state functional magnetic resonance imaging scans. fMRI-S4 capture short- and long- range temporal dependencies in the signal using 1D convolutions and the recently introduced state-space models S4. The proposed architecture is lightweight, sample-efficient and robust across tasks/datasets. We validate fMRI-S4 on the tasks of diagnosing major depressive disorder (MDD), autism spectrum disorder (ASD) and sex classifcation on three multi-site rs-fMRI datasets. We show that fMRI-S4 can outperform existing methods on all three tasks and can be trained as a plug&play model without special hyperpararameter tuning for each setting","PeriodicalId":344785,"journal":{"name":"MLCN@MICCAI","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131497078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients 早期帕金森患者MR脑部扫描补丁与全局图像的无监督异常检测

MLCN@MICCAI Pub Date : 2021-10-25 DOI: 10.1007/978-3-030-87586-2_4

V. Ramírez, N. Pinon, Florence Forbes, C. Lartizien, M. Dojat

引用次数: 4

Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling 动态自适应时空图卷积用于fMRI建模

MLCN@MICCAI Pub Date : 2021-09-26 DOI: 10.1007/978-3-030-87586-2_13

A. E. Gazzar, R. Thomas, G. Wingen

引用次数: 8

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