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Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation (2310.11320v1)

Published 17 Oct 2023 in eess.IV and cs.CV

Abstract: Volume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data. However, the challenges and practical applications extend beyond SSL to settings such as unsupervised domain adaptation (UDA) and semi-supervised domain generalization (SemiDG). This work aims to develop a generic SSL framework that can handle all three settings. We identify two main obstacles to achieving this goal in the existing SSL framework: 1) the weakness of capturing distribution-invariant features; and 2) the tendency for unlabeled data to be overwhelmed by labeled data, leading to over-fitting to the labeled data during training. To address these issues, we propose an Aggregating & Decoupling framework. The aggregating part consists of a Diffusion encoder that constructs a common knowledge set by extracting distribution-invariant features from aggregated information from multiple distributions/domains. The decoupling part consists of three decoders that decouple the training process with labeled and unlabeled data, thus avoiding over-fitting to labeled data, specific domains and classes. We evaluate our proposed framework on four benchmark datasets for SSL, Class-imbalanced SSL, UDA and SemiDG. The results showcase notable improvements compared to state-of-the-art methods across all four settings, indicating the potential of our framework to tackle more challenging SSL scenarios. Code and models are available at: https://github.com/xmed-lab/GenericSSL.

Citations (19)

Summary

  • The paper introduces the Aggregating Decoupling (AD) framework that uses a diffusion encoder and three decoder paths to combine labeled and unlabeled data effectively.
  • The approach employs Sampling-based Volumetric Data Augmentation (SVDA) to capture distribution-invariant features, achieving improvements like a 12.3 Dice score gain on the Synapse dataset.
  • The methodology mitigates overfitting and domain shifts, offering a robust pathway for adaptable segmentation in diverse clinical imaging scenarios.

Towards a Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation

The paper by Wang and Li presents an innovative approach to the complex challenge of semi-supervised volumetric medical image segmentation (SSVMIS) by proposing a unified framework that extends its application to unsupervised domain adaptation (UDA) and semi-supervised domain generalization (SemiDG). These advancements aim to address significant limitations in current SSVMIS methodologies, particularly regarding their ability to handle distribution and domain shifts, which frequently lead to overfitting and suboptimal performance in practical applications.

The authors introduce the Aggregating Decoupling (AD) framework, which is designed to manage labeled and unlabeled data effectively across domains and distributions. The framework comprises two main components: a diffusion encoder employed during the aggregating stage and a training process decoupled into three decoder paths. This design is proposed to overcome two primary obstacles in current SSVMIS methodologies: (1) the inadequacy in capturing distribution-invariant features, and (2) the dominance of labeled data leading to overfitting during training.

Overview of Methodology

In the AD framework, the aggregating stage utilizes a diffusion model that captures features invariant to the distribution by creating a shared encoder, thereby mining common knowledge within multiple domain inputs. This process utilizes Sampling-based Volumetric Data Augmentation (SVDA) to enrich the diversity of the data, enhancing the diffusion encoder’s ability to learn generalized features.

During the decoupling stage, the authors address overfitting by separating the training streams for labeled and unlabeled data. Three decoders are employed—each designed to manage specific aspects of domain biases. Two decoders work within the labeled data stream to produce pseudo-labels that are both domain- and class-unbiased, which subsequently inform the training of an additional decoder using unlabeled data.

Empirical Results and Significance

The AD framework demonstrates improved performance across multiple benchmarks. Noteworthy results include significant Dice score improvements of 12.3 on the Synapse dataset and 8.5 on the MR to CT setting of the MMWHS dataset. These results underscore the potential for the AD framework to extend well beyond traditional SSL applications, demonstrating efficacy in settings characterized by domain shifts.

Further, the paper identifies and provides a viable pathway for integrating diffusion models into semi-supervised learning frameworks, highlighting their capability in capturing robust, distribution-agnostic feature representations. This aspect showcases an innovative application of diffusion processes in a domain typically dominated by adversarial and transformation-based techniques.

Implications and Future Directions

The implications of this research are twofold. Practically, this framework offers a more flexible model for integrating labeled and unlabeled datasets from disparate clinical settings, which is pivotal for real-world medical imaging applications. Theoretically, the AD framework challenges existing paradigms by leveraging the latent space navigation abilities of diffusion encoders, advancing beyond the traditional teacher-student and cross-pseudo supervision models.

The paper suggests avenues for further research into optimizing diffusion processes within heterogeneous data environments and exploring the scalability of such frameworks to other medical imaging settings where domain diversity and class imbalances are prevalent.

Overall, the AD framework represents a robust step towards more generic, adaptable solutions in SSVMIS, with substantial implications for increasing the applicability and performance of machine learning models within the medical domain.