Summary BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability - ScienceDirect pdf.sciencedirectassets.com
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BayeSeg is a Bayesian model that improves medical image segmentation by incorporating shape information and enhancing generalizability.
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Slide Presentation (10 slides)
Key Points
- The BayeSeg framework is a Bayesian modeling approach designed to enhance the generalizability of deep learning models for medical image segmentation.
- BayeSeg addresses the challenge of cross-domain distribution shift, which can limit the real-world applicability of deep learning segmentation methods.
- The framework decomposes an image into shape and appearance variables and assigns hierarchical Bayesian priors to explicitly model domain-stable shape and domain-specific appearance information.
- BayeSeg achieves promising performance in cross-modality, cross-sequence, and cross-site settings for prostate and cardiac segmentation tasks.
- The interpretability of BayeSeg is demonstrated through explaining the posteriors and analyzing factors that affect generalization ability through ablation studies.
Summaries
17 word summary
BayeSeg is a Bayesian model that enhances medical image segmentation by incorporating shape information and improving generalizability.
53 word summary
BayeSeg is a Bayesian modeling framework for medical image segmentation that improves generalizability. It uses neural networks to decompose images into shape and appearance variables and assigns hierarchical Bayesian priors. BayeSeg explicitly models shape information, enhancing generalizability. It achieves interpretability and generalizability in cross-modality, cross-sequence, and cross-site settings, simplifying computation through variational inference.
128 word summary
BayeSeg is a Bayesian modeling framework that improves the generalizability of deep learning models for medical image segmentation. It decomposes images into shape and appearance variables and assigns hierarchical Bayesian priors to explicitly model domain-stable shape and domain-specific appearance information. The framework is implemented with neural networks and achieves enhanced interpretability and generalizability in medical image segmentation. Experimental results on prostate segmentation and cardiac segmentation tasks demonstrate its effectiveness in cross-modality, cross-sequence, and cross-site settings. BayeSeg extends previous statistical modeling approaches by explicitly modeling shape information, which represents organ structures and enhances generalizability. The variational inference of image and label in BayeSeg is achieved through a variational Bayesian approach, simplifying the computation. Overall, BayeSeg is a Bayesian segmentation framework that enhances interpretability and generalizability in medical image segmentation.
407 word summary
BayeSeg is a Bayesian modeling framework that aims to improve the generalizability of deep learning models for medical image segmentation. It addresses the challenge of cross-domain distribution shift that can limit the real-world applicability of these models. The framework decomposes an image into shape and appearance variables and assigns hierarchical Bayesian priors to explicitly model domain-stable shape and domain-specific appearance information. The segmentation is modeled as a locally smooth variable related to the shape. The framework, called deep Bayesian segmentation, is implemented with neural networks.
Experimental results on prostate segmentation and cardiac segmentation tasks demonstrate the effectiveness of BayeSeg in cross-modality, cross-sequence, and cross-site settings. The interpretability of BayeSeg is investigated through explaining the posteriors and analyzing factors that affect the generalization ability.
BayeSeg extends previous statistical modeling approaches by explicitly modeling shape information, which represents organ structures. The shape information is more likely to be a domain-invariant representation, leading to enhanced generalizability. The proposed framework jointly characterizes image and label statistics to extract interpretable shape representations for generalizable segmentation.
BayeSeg combines statistical modeling with image decomposition to achieve generalizable segmentation through a Bayesian framework. By modeling the relationship between segmentation and its ground truth, the framework improves model interpretability and generalization capability. The shape information extracted from the images represents domain-invariant organ structures, enhancing generalizability.
The variational inference of image and label in BayeSeg is achieved through a variational Bayesian approach. The posterior distributions are approximated by variational distributions. The variational posteriors have the same forms as the assigned conjugate priors, simplifying the computation.
The neural networks used in BayeSeg consist of ResNets for inferring the variational posteriors of the shape and appearance and a segmentation network for inferring the variational posterior of the segmentation.
BayeSeg achieves enhanced interpretability and generalizability in medical image segmentation. It utilizes joint modeling, explicit prior modeling, and image decomposition to capture both domain-specific appearance and domain-stable shape information. The framework is evaluated on prostate segmentation and cardiac segmentation tasks, demonstrating superior performance in various scenarios. Ablation studies validate the effectiveness of different components in BayeSeg, and the interpretability of the framework is demonstrated through the visualization of posteriors.
In conclusion, BayeSeg is a Bayesian segmentation framework that enhances the interpretability and generalizability of medical image segmentation. The joint modeling approach, along with explicit prior modeling, improves the model's ability to generalize to unseen domains. The results of the evaluation and ablation studies demonstrate the effectiveness of BayeSeg in various scenarios.
612 word summary
BayeSeg is a Bayesian modeling framework designed to improve the generalizability of deep learning models for medical image segmentation. It addresses the challenge of cross-domain distribution shift that can limit the real-world applicability of these models. BayeSeg proposes an interpretable Bayesian framework through Bayesian modeling of image and label statistics.
The framework decomposes an image into two variables: a spatial-correlated variable representing shape information and a spatial-variant variable representing appearance information. Hierarchical Bayesian priors are assigned to these variables to explicitly model domain-stable shape and domain-specific appearance information. The segmentation is modeled as a locally smooth variable only related to the shape. A variational Bayesian framework is used to infer the posterior distributions of these explainable variables. This framework, called deep Bayesian segmentation, is implemented with neural networks.
Experimental results on prostate segmentation and cardiac segmentation tasks demonstrate the effectiveness of BayeSeg. It achieves promising performance in cross-modality, cross-sequence, and cross-site settings. The interpretability of BayeSeg is also investigated by explaining the posteriors and analyzing factors that affect the generalization ability through ablation studies.
BayeSeg extends previous statistical modeling approaches by explicitly modeling shape information, which represents the structures of organs. The shape information is more likely to be a domain-invariant representation, leading to enhanced generalizability. The proposed framework jointly characterizes image and label statistics to extract interpretable shape representations for generalizable segmentation.
Domain generalization, which aims to make models trained on one or several source domains generalize well on unseen target domains, has gained attention in computer vision and medical image analysis. Current methods in domain generalization can be categorized into data-based, learning-based, and representation-based approaches. However, little attention has been paid to the interpretability of these domain-invariant features.
BayeSeg combines statistical modeling with image decomposition to achieve generalizable segmentation through a Bayesian framework. By modeling the relationship between segmentation and its ground truth, the framework improves model interpretability and generalization capability. The shape information extracted from the images represents domain-invariant organ structures, enhancing generalizability. The proposed method provides explanations in understandable terms to human experts, addressing ethical and legal requirements in clinical diagnosis and treatment.
Statistical modeling approaches have been widely used in image processing, including image restoration, classification, and segmentation. BayeSeg models the statistics of medical images by using spatially variant Gaussian priors to model spatial independence and simultaneous autoregressive models to model spatial correlation.
The variational inference of image and label in BayeSeg is achieved through a variational Bayesian approach. The posterior distributions are approximated by variational distributions. The variational posteriors have the same forms as the assigned conjugate priors, simplifying the computation. The variational loss is unfolded into several terms with semantic interpretation, providing insights into the modeling process.
The neural networks used in BayeSeg consist of ResNets for inferring the variational posteriors of the shape and appearance and a segmentation network for inferring the variational posterior of the segmentation.
BayeSeg achieves enhanced interpretability and generalizability in medical image segmentation. It utilizes joint modeling, explicit prior modeling, and image decomposition to capture both domain-specific appearance and domain-stable shape information. The framework is evaluated on prostate segmentation and cardiac segmentation tasks, demonstrating superior performance in various scenarios. Ablation studies validate the effectiveness of different components in BayeSeg, and the interpretability of the framework is demonstrated through the visualization of posteriors.
In conclusion, BayeSeg is a Bayesian segmentation framework that enhances the interpretability and generalizability of medical image segmentation. The joint modeling approach, along with explicit prior modeling, improves the model's ability to generalize to unseen domains. The results of the evaluation and ablation studies demonstrate the effectiveness of BayeSeg in various scenarios. Future work could explore model selection and causal deep learning for further improvements in domain generalization.
993 word summary
The BayeSeg framework is a Bayesian modeling approach designed to enhance the generalizability of deep learning models for medical image segmentation. The framework addresses the challenge of cross-domain distribution shift, which can limit the real-world applicability of deep learning segmentation methods. Previous methods have focused on extracting domain-invariant representations for domain generalization, but the interpretability of these features remains a challenge. BayeSeg aims to address this problem by proposing an interpretable Bayesian framework through Bayesian modeling of image and label statistics.
The framework decomposes an image into two variables: a spatial-correlated variable representing shape information, and a spatial-variant variable representing appearance information. Hierarchical Bayesian priors are assigned to these variables to explicitly model the domain-stable shape and domain-specific appearance information. The segmentation is modeled as a locally smooth variable only related to the shape. A variational Bayesian framework is then used to infer the posterior distributions of these explainable variables. The framework is implemented with neural networks, referred to as deep Bayesian segmentation.
Experimental results on prostate segmentation and cardiac segmentation tasks demonstrate the effectiveness of the proposed method. The framework achieves promising performance in cross-modality, cross-sequence, and cross-site settings. The interpretability of BayeSeg is also investigated by explaining the posteriors and analyzing factors that affect the generalization ability through ablation studies.
Statistical modeling is crucial for improving model interpretability and generalization capability. Previous works have focused on Gaussian prior modeling, Markov random field modeling, and sparsity modeling. BayeSeg extends these approaches by exploring explicit modeling for shape information, which represents the structures of organs. The shape information is more likely to be a domain-invariant representation, leading to enhanced generalizability. The proposed framework jointly characterizes image and label statistics to extract interpretable shape representations for generalizable segmentation.
Domain generalization, which aims to make models trained on one or several source domains generalize well on unseen target domains, has gained increasing attention in computer vision and medical image analysis. Current methods in domain generalization can be categorized into data-based, learning-based, and representation-based approaches. Data-based methods enrich the diversity of training data through data augmentation or data generalization. Learning-based methods combine knowledge from training domains by specific strategies such as ensemble learning and meta-learning. Representation-based methods extract domain-invariant or domain-irrelevant features through feature alignment or feature disentanglement. However, little attention has been paid to the interpretability of these domain-invariant features.
BayeSeg combines statistical modeling with image decomposition to achieve generalizable segmentation through a Bayesian framework. By modeling the relationship between segmentation and its ground truth, the framework improves model interpretability and generalization capability. The shape information extracted from the images represents domain-invariant organ structures, enhancing generalizability. The proposed method provides explanations in understandable terms to human experts, addressing ethical and legal requirements in clinical diagnosis and treatment.
Statistical modeling approaches have been widely used in image processing, including image restoration, image classification, and image segmentation. Early works focused on Gaussian prior modeling and Markov random field modeling, but these models had limitations in capturing the relationship among pixels. Spatial correlation modeling and sparsity modeling were then developed to overcome these limitations. The proposed BayeSeg framework models the statistics of medical images by using spatially variant Gaussian priors to model spatial independence and simultaneous autoregressive models to model spatial correlation.
The variational inference of image and label in BayeSeg is achieved through a VB approach. The posterior distributions are approximated by variational distributions. The variational posteriors have the same forms as the assigned conjugate priors, simplifying the computation. The variational loss is unfolded into several terms with semantic interpretation, providing insights into the modeling process.
The neural networks used in BayeSeg consist of ResNets for inferring the variational posteriors of the shape and appearance, and a segmentation network for inferring the variational posterior of the segmentation. The network architecture and training
BayeSeg is a Bayesian segmentation framework that aims to enhance the interpretability and generalizability of medical image segmentation. The framework utilizes a joint modeling approach that incorporates both image and label statistics. By decomposing images into appearance and shape and assigning hierarchical Bayesian priors, BayeSeg is able to capture both domain-specific appearance and domain-stable shape information. The segmentation predictions are then generated from the shape variable.
The effectiveness of BayeSeg was evaluated on two tasks: prostate segmentation and cardiac segmentation. For prostate segmentation, three datasets composed of six domains were used for training and testing. The results showed that BayeSeg outperformed other domain generalization approaches, achieving an average Dice score of 77.5. In particular, BayeSeg demonstrated superior performance on difficult target domains with wild bias fields.
For cardiac segmentation, five datasets consisting of six domains were used. BayeSeg achieved a Dice score of 77.5, surpassing the second-best method by 7.7. The results also showed that BayeSeg outperformed other methods in cross-sequence, cross-site, and cross-modality scenarios.
Ablation studies were conducted to explore the effectiveness of different components in BayeSeg. The results showed that the variational loss played a key role in enhancing the generalizability of BayeSeg. Introducing stochastic mapping alone did not significantly improve performance, but it was a prerequisite for applying the variational loss. The results also showed that the choice of priors for appearance and shape variables had a significant impact on model generalizability.
The interpretability of BayeSeg was demonstrated through the visualization of posteriors. The results showed that BayeSeg provided semantic interpretation of the extracted representations, enhancing the comprehensibility and interpretability of the model.
The benefits of explicit prior modeling and joint modeling of image and label statistics were validated through ablation studies. The results showed that these components improved the generalization capability of BayeSeg.
In conclusion, BayeSeg is a Bayesian segmentation framework that enhances the interpretability and generalizability of medical image segmentation. The joint modeling approach, along with explicit prior modeling, improves the model's ability to generalize to unseen domains. The results of the evaluation and ablation studies demonstrate the effectiveness of BayeSeg in various scenarios. Future work could explore model selection and causal deep learning for further improvements in domain generalization.