Annual Conference on Medical Image Understanding and Analysis最新文献

A New Approach for Identifying Skin Diseases from Dermatological RGB Images Using Source Separation 利用源分离从皮肤病 RGB 图像中识别皮肤病的新方法

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2023-09-21 DOI: 10.1007/978-3-031-48593-0_18

Mustapha Zokay, Hicham Saylani

引用次数: 0

Removing Specular Reflection in Multispectral Dermatological Images Using Blind Source Separation 利用盲源分离去除多光谱皮肤图像中的镜面反射

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2023-06-02 DOI: 10.1007/978-3-031-12053-4_54

Mustapha Zokay, Hicham Saylani

引用次数: 0

FCN-Transformer Feature Fusion for Polyp Segmentation 基于FCN-Transformer特征融合的息肉分割

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-08-17 DOI: 10.1007/978-3-031-12053-4_65

Edward Sanderson, B. Matuszewski

引用次数: 43

Implicit U-Net for volumetric medical image segmentation 隐式U-Net用于体积医学图像分割

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-30 DOI: 10.48550/arXiv.2206.15217

Sergio Naval Marimont, G. Tarroni

引用次数: 0

Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction 基于深度学习减少屏气的更快扩散心脏MRI

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-21 DOI: 10.48550/arXiv.2206.10543

Michael Tanzer, P. Ferreira, A. Scott, Z. Khalique, Maria Dwornik, D. Pennell, Guang Yang, D. Rueckert, S. Nielles-Vallespin

引用次数: 3

Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma Grading 利用深度学习中的不确定性进行胰腺腺癌分级

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-15 DOI: 10.48550/arXiv.2206.08787

Biraja Ghoshal, Bhargab Ghoshal, A. Tucker

引用次数: 1

A Deep Generative Model of Neonatal Cortical Surface Development 新生儿皮质表面发育的深层生成模型

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-15 DOI: 10.48550/arXiv.2206.07542

Abdulah Fawaz, Logan Z. J. Williams, A. Edwards, E. Robinson

{"title":"A Deep Generative Model of Neonatal Cortical Surface Development","authors":"Abdulah Fawaz, Logan Z. J. Williams, A. Edwards, E. Robinson","doi":"10.48550/arXiv.2206.07542","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07542","url":null,"abstract":". The neonatal cortical surface is known to be aﬀected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional ﬁlters are inappro-priate on non-ﬂat topologies. To close this gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to translate spher-icalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between diﬀerent stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation ( > 37 weeks gestation), validated through comparison with a trained term/preterm classiﬁer. Simulated diﬀerences in cortical maturation are consistent with observations in the literature.","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130528474","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

Fitting Segmentation Networks on Varying Image Resolutions using Splatting 用飞溅法拟合不同图像分辨率的分割网络

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-13 DOI: 10.48550/arXiv.2206.06445

Mikael Brudfors, Yael Balbastre, J. Ashburner, Geraint Rees, P. Nachev, S. Ourselin, M. Cardoso

{"title":"Fitting Segmentation Networks on Varying Image Resolutions using Splatting","authors":"Mikael Brudfors, Yael Balbastre, J. Ashburner, Geraint Rees, P. Nachev, S. Ourselin, M. Cardoso","doi":"10.48550/arXiv.2206.06445","DOIUrl":"https://doi.org/10.48550/arXiv.2206.06445","url":null,"abstract":"Data used in image segmentation are not always defined on the same grid. This is particularly true for medical images, where the resolution, field-of-view and orientation can differ across channels and subjects. Images and labels are therefore commonly resampled onto the same grid, as a pre-processing step. However, the resampling operation introduces partial volume effects and blurring, thereby changing the effective resolution and reducing the contrast between structures. In this paper we propose a splat layer, which automatically handles resolution mismatches in the input data. This layer pushes each image onto a mean space where the forward pass is performed. As the splat operator is the adjoint to the resampling operator, the mean-space prediction can be pulled back to the native label space, where the loss function is computed. Thus, the need for explicit resolution adjustment using interpolation is removed. We show on two publicly available datasets, with simulated and real multi-modal magnetic resonance images, that this model improves segmentation results compared to resampling as a pre-processing step.","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134184652","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: 0

Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification 深度学习在皮肤镜下皮肤病变分类中的形状偏差研究

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-13 DOI: 10.48550/arXiv.2206.06466

Adriano Lucieri, Fabian Schmeisser, Christoph Balada, Shoaib Ahmed Siddiqui, A. Dengel, Sheraz Ahmed

{"title":"Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification","authors":"Adriano Lucieri, Fabian Schmeisser, Christoph Balada, Shoaib Ahmed Siddiqui, A. Dengel, Sheraz Ahmed","doi":"10.48550/arXiv.2206.06466","DOIUrl":"https://doi.org/10.48550/arXiv.2206.06466","url":null,"abstract":"It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to align deep models' decision-making processes with the fundamental properties of human vision. The reliance on shape features is primarily expected to improve the robustness of these models under covariate shift. In this paper, we revisit the significance of shape-biases for the classification of skin lesion images. Our analysis shows that different skin lesion datasets exhibit varying biases towards individual image features. Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation. This indicates that these features are still represented in the learnt embedding spaces of the models, but not used for classification. In addition, the spectral analysis of different datasets shows that in contrast to common visual recognition, dermoscopic skin lesion classification, by nature, is reliant on complex feature combinations beyond shape-bias. As a natural consequence, shifting away from the prevalent desire of shape-biasing models can even improve skin lesion classifiers in some cases.","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134464679","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: 0

Recursive Deformable Image Registration Network with Mutual Attention 具有相互关注的递归可变形图像配准网络

Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-04 DOI: 10.48550/arXiv.2206.01863

Jian-Qing Zheng, Ziyang Wang, Baoru Huang, N. Lim, T. Vincent, B. Papież

{"title":"Recursive Deformable Image Registration Network with Mutual Attention","authors":"Jian-Qing Zheng, Ziyang Wang, Baoru Huang, N. Lim, T. Vincent, B. Papież","doi":"10.48550/arXiv.2206.01863","DOIUrl":"https://doi.org/10.48550/arXiv.2206.01863","url":null,"abstract":"Deformable image registration, estimating the spatial transformation between different images, is an important task in medical imaging. Many previous studies have used learning-based methods for multi-stage registration to perform 3D image registration to improve performance. The performance of the multi-stage approach, however, is limited by the size of the receptive field where complex motion does not occur at a single spatial scale. We propose a new registration network combining recursive network architecture and mutual attention mechanism to overcome these limitations. Compared with the state-of-the-art deep learning methods, our network based on the recursive structure achieves the highest accuracy in lung Computed Tomography (CT) data set (Dice score of 92% and average surface distance of 3.8mm for lungs) and one of the most accurate results in abdominal CT data set with 9 organs of various sizes (Dice score of 55% and average surface distance of 7.8mm). We also showed that adding 3 recursive networks is sufficient to achieve the state-of-the-art results without a significant increase in the inference time.","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115511069","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