Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging

Khandelwal, Pulkit; Yushkevich, Paul A.

doi:10.1007/978-3-030-60548-3_8

Cited by 27 publications

(15 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In domain generalization task (Task 2), the models trained with only non‐COVID‐19 dataset fail to segment infections. One can introduce more advanced model‐agnostic learning methods to handle the domain gap, such as meta‐learning 41,42 . In knowledge transfer task (Task 3), simply fusing non‐COVID‐19 and COVID‐19 dataset with the SOTA network could bias the model to learn more non‐COVID‐19 features.…”

Section: Resultsmentioning

confidence: 99%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

217

171

View full text Add to dashboard Cite

Purpose Accurate segmentation of lung and infection in COVID‐19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID‐19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods To promote the development of data‐efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID‐19 cases, which contain current active research areas, for example, few‐shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results Based on the state‐of‐the‐art network, we provide more than 40 pretrained baseline models, which not only serve as out‐of‐the‐box segmentation tools but also save computational time for researchers who are interested in COVID‐19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions To the best of our knowledge, this work presents the first data‐efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID‐19 CT segmentation with limited data.

show abstract

Section: Resultsmentioning

confidence: 99%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

217

171

View full text Add to dashboard Cite

show abstract

“…In other words, meta-learning seeks to improve the learning algorithm itself with either task-agnostic or task-specific prior knowledge and thus can improve both data and computational efficiency. Thus, there is a rapid growth in interest in meta-learning and its various applications, including medical image segmentation with limited supervision [325]. Utilizing meta-loss on a small set of labeled data has shown promising results in few-shot learning [324], [326], [327].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Wang

2021

IEEE Access

View full text Add to dashboard Cite

Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks. The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling. Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful application of deep learning models in medical image segmentation. However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed. In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies. We also highlight several problems in this field, discussed future directions observing further investigations.

show abstract

“…Channel-spatial excitation AnatomyNet (CE + SE); (6.) 3D Unet-like network [15]; (7.) VoxResNet [8]; (8.)…”

Section: Methodsunclassified

Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w MRI of Human Brain Hemispheres

Khandelwal¹,

Sadaghiani²,

Duong³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for visualizing and characterizing detailed neuroanatomy. However, automated cortical segmentation methods in ex vivo MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution 7 Tesla dataset of 32 ex vivo human brain specimens. We benchmark the cortical mantle segmentation performance of nine neural network architectures, trained and evaluated using manually-segmented 3D patches sampled from specific cortical regions, and show excellent generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at different magnetic field strength and imaging sequences. Finally, we provide cortical thickness measurements across key regions in 3D ex vivo human brain images. Our code and processed datasets are publicly available at here.

show abstract

Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging

Cited by 27 publications

References 20 publications

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w MRI of Human Brain Hemispheres

Contact Info

Product

Resources

About