Automating Weak Label Generation for Medical Image Segmentation to Boost Performance in Label-Scarce Settings

Core Concepts

A pipeline that automatically generates high-quality weak labels for unlabeled medical images using foundation models, enabling significant performance boosts in label-scarce segmentation tasks.

Abstract

The authors present a new pipeline that tackles the challenge of limited labeled data in medical image segmentation by leveraging the power of foundation models like the Segment Anything Model (SAM) and its medical counterpart, MedSAM. The key aspects of the pipeline are: Training an initial segmentation model on a small set of gold-standard labels (25-50) to generate coarse labels for the unlabeled data. Using the coarse labels to automatically select input prompts (bounding boxes or points) for MedSAM, which then generates high-quality weak labels for the unlabeled data. Combining the gold-standard and weak-labeled data to train a final segmentation model, leading to significant performance improvements. The pipeline is evaluated on three medical imaging datasets (BUSI, ISIC, and CANDID-PTX) covering ultrasound, dermoscopy, and X-ray modalities. The authors demonstrate that their method can achieve performance boosts ranging from 6.6% to 72.3% in DICE score compared to using only the limited gold-standard labels. The authors also explore the use of synthetic data generated by diffusion models to further augment the training data and observe additional performance gains. Ablation studies are conducted to analyze the impact of different label-scarce settings and input selection techniques. The proposed pipeline effectively addresses the challenge of label scarcity in medical image segmentation by leveraging foundation models to auto-generate high-quality weak labels, enabling the training of more accurate models while minimizing the need for expensive manual annotation.

Stats

"The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to train deep learning models for medical imaging tasks." "We conduct experiments on label-scarce settings for multiple tasks pertaining to modalities ranging from ultrasound, dermatology, and X-rays to demonstrate the usefulness of our pipeline." "We achieve improvements of up to 73.3%, with more dramatic improvements where the initial DICE score was lower."

Quotes

"Our pipeline has the ability to generate weak labels for any unlabeled medical image and subsequently use it to augment label-scarce datasets." "This automation eliminates the manual prompting step in MedSAM, creating a streamlined process for generating labels for both real and synthetic images, regardless of quantity."

Key Insights Distilled From

Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

by Tanvi Deshpa... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17033.pdf

Deeper Inquiries

How could this pipeline be extended to handle 3D medical imaging tasks, which often require more complex segmentation?

To extend this pipeline to handle 3D medical imaging tasks, several adjustments and enhancements would be necessary. Firstly, the model architecture would need to be modified to accommodate 3D data, as opposed to the 2D data used in the current pipeline. This may involve utilizing 3D convolutional neural networks (CNNs) or other architectures specifically designed for volumetric data. Additionally, the input selection process would need to be adapted to account for the additional dimension in the data. Techniques such as volumetric bounding boxes or point prompts could be explored to guide the segmentation model in identifying the regions of interest in the 3D images. Furthermore, the generation of weak labels for 3D data may require more sophisticated methods due to the increased complexity of segmentation tasks in volumetric images. Techniques like incorporating contextual information across multiple slices or volumes could be beneficial in improving the quality of weak labels generated for 3D medical imaging tasks.

What are the potential limitations of using weak labels generated by foundation models, and how could the quality of these labels be further improved?

One potential limitation of using weak labels generated by foundation models is the inherent bias or inaccuracies that may be present in the generated labels. Foundation models, while powerful, may not always capture the nuances and intricacies of medical imaging tasks, leading to suboptimal weak labels. To improve the quality of weak labels generated by foundation models, several strategies can be employed: Fine-tuning on domain-specific data: Adapting the foundation model to the specific characteristics of medical imaging data through fine-tuning can enhance the accuracy of weak labels. Ensemble methods: Combining weak labels generated by multiple foundation models or techniques can help mitigate individual model biases and improve label quality. Human validation: Incorporating human experts to validate and refine weak labels can ensure higher accuracy and reliability in the generated labels. Active learning: Implementing active learning strategies to iteratively improve weak labels by selecting the most informative samples for manual annotation can enhance label quality over time. By implementing these strategies, the quality of weak labels generated by foundation models can be enhanced, leading to more effective training of segmentation models in label-scarce scenarios.

Given the success of this approach in label-scarce settings, how could it be leveraged to enhance human-in-the-loop annotation workflows for medical image segmentation?

The success of this approach in generating weak labels for medical image segmentation can significantly enhance human-in-the-loop annotation workflows by reducing the manual effort required for labeling large datasets. Here are some ways this approach could be leveraged to improve human-in-the-loop annotation workflows: Semi-supervised learning: Weak labels generated by the pipeline can be used in conjunction with a small set of gold-standard labels to train semi-supervised learning models. This can reduce the burden on human annotators while maintaining model performance. Active learning: Weak labels can be used to identify challenging or uncertain samples for human annotation, guiding annotators to focus on the most informative data points and improving the overall quality of the labeled dataset. Continuous improvement: By iteratively refining weak labels through human validation and feedback, the pipeline can adapt and improve over time, leading to more accurate weak labels and better model performance. Scalability: The automated generation of weak labels can scale annotation workflows to handle larger datasets efficiently, enabling faster model development and deployment in real-world medical imaging tasks. By integrating this approach into human-in-the-loop annotation workflows, organizations can streamline the labeling process, reduce costs, and accelerate the development of robust segmentation models for medical image analysis.