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Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning.

Deep learning in medical image analysis and multimodal learning for clinical decision support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, held in conjunction with MICCAI 2017 Quebec City, QC,...

Thung KH, Yap PT, Shen D.
PMID: 29104963
Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2017). 2017 Sep;10553:160-168. doi: 10.1007/978-3-319-67558-9_19. Epub 2017 Sep 09.

Utilization of biomedical data from multiple modalities improves the diagnostic accuracy of neurodegenerative diseases. However, multi-modality data are often incomplete because not all data can be collected for every individual. When using such incomplete data for diagnosis, current approaches...

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Thung KH, Yap PT, Shen D. Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2017). 2017;10553:160-168doi: 10.1007/978-3-319-67558-9_19.
Thung, K. H., Yap, P. T., & Shen, D. (2017). Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning. Deep learning in medical image analysis and multimodal learning for clinical decision support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, held in conjunction with MICCAI 2017 Quebec City, QC,..., 10553160-168. https://doi.org/10.1007/978-3-319-67558-9_19
Thung, Kim-Han, et al. "Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning." Deep learning in medical image analysis and multimodal learning for clinical decision support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, held in conjunction with MICCAI 2017 Quebec City, QC,... vol. 10553 (2017): 160-168. doi: https://doi.org/10.1007/978-3-319-67558-9_19
Thung KH, Yap PT, Shen D. Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2017). 2017 Sep;10553:160-168. doi: 10.1007/978-3-319-67558-9_19. Epub 2017 Sep 09. PMID: 29104963; PMCID: PMC5666687.
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Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S...

Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK.
PMID: 30450490
Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:83-91. doi: 10.1007/978-3-030-00889-5_10. Epub 2018 Sep 20.

Deep learning with convolutional neural networks (CNN) has achieved unprecedented success in segmentation, however it requires large training data, which is expensive to obtain. Active Learning (AL) frameworks can facilitate major improvements in CNN performance with intelligent selection of...

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Sourati J, Gholipour A, Dy JG, et al. Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018;11045:83-91doi: 10.1007/978-3-030-00889-5_10.
Sourati, J., Gholipour, A., Dy, J. G., Kurugol, S., & Warfield, S. K. (2018). Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S..., 1104583-91. https://doi.org/10.1007/978-3-030-00889-5_10
Sourati, Jamshid, et al. "Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S... vol. 11045 (2018): 83-91. doi: https://doi.org/10.1007/978-3-030-00889-5_10
Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK. Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:83-91. doi: 10.1007/978-3-030-00889-5_10. Epub 2018 Sep 20. PMID: 30450490; PMCID: PMC6235453.
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Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S...

Lamash Y, Kurugol S, Warfield SK.
PMID: 30450491
Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:218-226. doi: 10.1007/978-3-030-00889-5_25. Epub 2018 Sep 20.

We propose a 3D residual convolutional neural network (CNN) algorithm with an integrated distance prior for segmenting the small bowel lumen and wall to enable extraction of pediatric Crohns disease (pCD) imaging markers from T1-weighted contrast-enhanced MR images. Our...

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Lamash Y, Kurugol S, Warfield SK. Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018;11045:218-226doi: 10.1007/978-3-030-00889-5_25.
Lamash, Y., Kurugol, S., & Warfield, S. K. (2018). Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S..., 11045218-226. https://doi.org/10.1007/978-3-030-00889-5_25
Lamash, Yechiel, et al. "Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S... vol. 11045 (2018): 218-226. doi: https://doi.org/10.1007/978-3-030-00889-5_25
Lamash Y, Kurugol S, Warfield SK. Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:218-226. doi: 10.1007/978-3-030-00889-5_25. Epub 2018 Sep 20. PMID: 30450491; PMCID: PMC6235454.
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Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge.

Medical image analysis

Ivantsits M, Goubergrits L, Kuhnigk JM, Huellebrand M, Bruening J, Kossen T, Pfahringer B, Schaller J, Spuler A, Kuehne T, Jia Y, Li X, Shit S, Menze B, Su Z, Ma J, Nie Z, Jain K, Liu Y, Lin Y, Hennemuth A.
PMID: 34998111
Med Image Anal. 2021 Dec 16;77:102333. doi: 10.1016/j.media.2021.102333. Epub 2021 Dec 16.

The Cerebral Aneurysm Detection and Analysis (CADA) challenge was organized to support the development and benchmarking of algorithms for detecting, analyzing, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided...

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Ivantsits M, Goubergrits L, Kuhnigk JM, et al. Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge. Med Image Anal. 2021;77:102333doi: 10.1016/j.media.2021.102333.
Ivantsits, M., Goubergrits, L., Kuhnigk, J. M., Huellebrand, M., Bruening, J., Kossen, T., Pfahringer, B., Schaller, J., Spuler, A., Kuehne, T., Jia, Y., Li, X., Shit, S., Menze, B., Su, Z., Ma, J., Nie, Z., Jain, K., Liu, Y., Lin, Y., & Hennemuth, A. (2021). Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge. Medical image analysis, 77102333. https://doi.org/10.1016/j.media.2021.102333
Ivantsits, Matthias, et al. "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge." Medical image analysis vol. 77 (2021): 102333. doi: https://doi.org/10.1016/j.media.2021.102333
Ivantsits M, Goubergrits L, Kuhnigk JM, Huellebrand M, Bruening J, Kossen T, Pfahringer B, Schaller J, Spuler A, Kuehne T, Jia Y, Li X, Shit S, Menze B, Su Z, Ma J, Nie Z, Jain K, Liu Y, Lin Y, Hennemuth A. Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge. Med Image Anal. 2021 Dec 16;77:102333. doi: 10.1016/j.media.2021.102333. Epub 2021 Dec 16. PMID: 34998111.
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Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity.

Medical image analysis

Anastasiou A, Cribben I, Fryzlewicz P.
PMID: 34700242
Med Image Anal. 2022 Jan;75:102252. doi: 10.1016/j.media.2021.102252. Epub 2021 Sep 30.

Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several...

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Anastasiou A, Cribben I, Fryzlewicz P. Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Med Image Anal. 2021;75:102252doi: 10.1016/j.media.2021.102252.
Anastasiou, A., Cribben, I., & Fryzlewicz, P. (2022). Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Medical image analysis, 75102252. https://doi.org/10.1016/j.media.2021.102252
Anastasiou, Andreas, et al. "Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity." Medical image analysis vol. 75 (2022): 102252. doi: https://doi.org/10.1016/j.media.2021.102252
Anastasiou A, Cribben I, Fryzlewicz P. Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Med Image Anal. 2022 Jan;75:102252. doi: 10.1016/j.media.2021.102252. Epub 2021 Sep 30. PMID: 34700242.
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Characterizing interactions between cardiac shape and deformation by non-linear manifold learning.

Medical image analysis

Maxime DF, Pamela M, Patrick C, Nicolas D.
PMID: 34731772
Med Image Anal. 2022 Jan;75:102278. doi: 10.1016/j.media.2021.102278. Epub 2021 Oct 23.

In clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which...

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Maxime DF, Pamela M, Patrick C, et al. Characterizing interactions between cardiac shape and deformation by non-linear manifold learning. Med Image Anal. 2021;75:102278doi: 10.1016/j.media.2021.102278.
Maxime, D. F., Pamela, M., Patrick, C., & Nicolas, D. (2022). Characterizing interactions between cardiac shape and deformation by non-linear manifold learning. Medical image analysis, 75102278. https://doi.org/10.1016/j.media.2021.102278
Maxime, Di Folco, et al. "Characterizing interactions between cardiac shape and deformation by non-linear manifold learning." Medical image analysis vol. 75 (2022): 102278. doi: https://doi.org/10.1016/j.media.2021.102278
Maxime DF, Pamela M, Patrick C, Nicolas D. Characterizing interactions between cardiac shape and deformation by non-linear manifold learning. Med Image Anal. 2022 Jan;75:102278. doi: 10.1016/j.media.2021.102278. Epub 2021 Oct 23. PMID: 34731772.
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Analysis of the ISIC image datasets: Usage, benchmarks and recommendations.

Medical image analysis

Cassidy B, Kendrick C, Brodzicki A, Jaworek-Korjakowska J, Yap MH.
PMID: 34852988
Med Image Anal. 2022 Jan;75:102305. doi: 10.1016/j.media.2021.102305. Epub 2021 Nov 16.

The International Skin Imaging Collaboration (ISIC) datasets have become a leading repository for researchers in machine learning for medical image analysis, especially in the field of skin cancer detection and malignancy assessment. They contain tens of thousands of dermoscopic...

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Cassidy B, Kendrick C, Brodzicki A, et al. Analysis of the ISIC image datasets: Usage, benchmarks and recommendations. Med Image Anal. 2021;75:102305doi: 10.1016/j.media.2021.102305.
Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., & Yap, M. H. (2022). Analysis of the ISIC image datasets: Usage, benchmarks and recommendations. Medical image analysis, 75102305. https://doi.org/10.1016/j.media.2021.102305
Cassidy, Bill, et al. "Analysis of the ISIC image datasets: Usage, benchmarks and recommendations." Medical image analysis vol. 75 (2022): 102305. doi: https://doi.org/10.1016/j.media.2021.102305
Cassidy B, Kendrick C, Brodzicki A, Jaworek-Korjakowska J, Yap MH. Analysis of the ISIC image datasets: Usage, benchmarks and recommendations. Med Image Anal. 2022 Jan;75:102305. doi: 10.1016/j.media.2021.102305. Epub 2021 Nov 16. PMID: 34852988.
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Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation.

Medical image analysis

Wang R, Chaudhari P, Davatzikos C.
PMID: 34871931
Med Image Anal. 2021 Nov 26;76:102309. doi: 10.1016/j.media.2021.102309. Epub 2021 Nov 26.

Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisition protocols at different sites presents a significant domain...

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Wang R, Chaudhari P, Davatzikos C. Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation. Med Image Anal. 2021;76:102309doi: 10.1016/j.media.2021.102309.
Wang, R., Chaudhari, P., & Davatzikos, C. (2021). Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation. Medical image analysis, 76102309. https://doi.org/10.1016/j.media.2021.102309
Wang, Rongguang, et al. "Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation." Medical image analysis vol. 76 (2021): 102309. doi: https://doi.org/10.1016/j.media.2021.102309
Wang R, Chaudhari P, Davatzikos C. Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation. Med Image Anal. 2021 Nov 26;76:102309. doi: 10.1016/j.media.2021.102309. Epub 2021 Nov 26. PMID: 34871931.
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AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information.

Medical image analysis

Li L, Zimmer VA, Schnabel JA, Zhuang X.
PMID: 34875581
Med Image Anal. 2021 Nov 16;76:102303. doi: 10.1016/j.media.2021.102303. Epub 2021 Nov 16.

Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. The automatic segmentation is however still challenging due to the poor image quality, the various LA...

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Li L, Zimmer VA, Schnabel JA, et al. AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Med Image Anal. 2021;76:102303doi: 10.1016/j.media.2021.102303.
Li, L., Zimmer, V. A., Schnabel, J. A., & Zhuang, X. (2021). AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Medical image analysis, 76102303. https://doi.org/10.1016/j.media.2021.102303
Li, Lei, et al. "AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information." Medical image analysis vol. 76 (2021): 102303. doi: https://doi.org/10.1016/j.media.2021.102303
Li L, Zimmer VA, Schnabel JA, Zhuang X. AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Med Image Anal. 2021 Nov 16;76:102303. doi: 10.1016/j.media.2021.102303. Epub 2021 Nov 16. PMID: 34875581.
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Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching.

Medical image analysis

Liu Y, Wang X, Wu Z, López-Linares K, Macía I, Ru X, Zhao H, González Ballester MA, Zhang C.
PMID: 34743037
Med Image Anal. 2022 Jan;75:102249. doi: 10.1016/j.media.2021.102249. Epub 2021 Oct 08.

Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling...

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Liu Y, Wang X, Wu Z, et al. Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching. Med Image Anal. 2021;75:102249doi: 10.1016/j.media.2021.102249.
Liu, Y., Wang, X., Wu, Z., López-Linares, K., Macía, I., Ru, X., Zhao, H., González Ballester, M. A., & Zhang, C. (2022). Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching. Medical image analysis, 75102249. https://doi.org/10.1016/j.media.2021.102249
Liu, Yue, et al. "Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching." Medical image analysis vol. 75 (2022): 102249. doi: https://doi.org/10.1016/j.media.2021.102249
Liu Y, Wang X, Wu Z, López-Linares K, Macía I, Ru X, Zhao H, González Ballester MA, Zhang C. Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching. Med Image Anal. 2022 Jan;75:102249. doi: 10.1016/j.media.2021.102249. Epub 2021 Oct 08. PMID: 34743037.
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DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography.

Medical image analysis

Zhou B, Chen X, Zhou SK, Duncan JS, Liu C.
PMID: 34758443
Med Image Anal. 2022 Jan;75:102289. doi: 10.1016/j.media.2021.102289. Epub 2021 Oct 29.

Sparse-view computed tomography (SVCT) aims to reconstruct a cross-sectional image using a reduced number of x-ray projections. While SVCT can efficiently reduce the radiation dose, the reconstruction suffers from severe streak artifacts, and the artifacts are further amplified with...

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Zhou B, Chen X, Zhou SK, et al. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Med Image Anal. 2021;75:102289doi: 10.1016/j.media.2021.102289.
Zhou, B., Chen, X., Zhou, S. K., Duncan, J. S., & Liu, C. (2022). DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Medical image analysis, 75102289. https://doi.org/10.1016/j.media.2021.102289
Zhou, Bo, et al. "DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography." Medical image analysis vol. 75 (2022): 102289. doi: https://doi.org/10.1016/j.media.2021.102289
Zhou B, Chen X, Zhou SK, Duncan JS, Liu C. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Med Image Anal. 2022 Jan;75:102289. doi: 10.1016/j.media.2021.102289. Epub 2021 Oct 29. PMID: 34758443.
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Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue.

Medical image analysis

Shao S, Pei Z, Chen W, Zhu W, Wu X, Sun D, Zhang B.
PMID: 35016079
Med Image Anal. 2021 Dec 25;77:102338. doi: 10.1016/j.media.2021.102338. Epub 2021 Dec 25.

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios. One widely adopted assumption of depth and ego-motion self-supervised learning is that the image brightness remains constant...

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Shao S, Pei Z, Chen W, et al. Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue. Med Image Anal. 2021;77:102338doi: 10.1016/j.media.2021.102338.
Shao, S., Pei, Z., Chen, W., Zhu, W., Wu, X., Sun, D., & Zhang, B. (2021). Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue. Medical image analysis, 77102338. https://doi.org/10.1016/j.media.2021.102338
Shao, Shuwei, et al. "Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue." Medical image analysis vol. 77 (2021): 102338. doi: https://doi.org/10.1016/j.media.2021.102338
Shao S, Pei Z, Chen W, Zhu W, Wu X, Sun D, Zhang B. Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue. Med Image Anal. 2021 Dec 25;77:102338. doi: 10.1016/j.media.2021.102338. Epub 2021 Dec 25. PMID: 35016079.
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Showing 1 to 12 of 449 entries