Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network (1806.05473v4)

Abstract: Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.

• The paper presents an active learning framework that uses cGANs to generate diverse samples and Bayesian neural networks to select the most informative ones for training.
• Experiments show the method achieves near state-of-the-art accuracy using only about 35% of the labeled data, significantly reducing annotation effort.
• This approach has significant implications for deploying efficient deep learning models in medical imaging where labeled data is scarce, improving data utilization and model robustness.

Efficient Active Learning for Image Classification and Segmentation Using cGANs

The paper presents a framework for enhancing the training of deep learning (DL) systems used in medical image classification and segmentation, specifically in scenarios where there is limited availability of labeled data. The authors propose a novel active learning (AL) mechanism that identifies and selects the most informative samples using a conditional generative adversarial network (cGAN) and Bayesian neural network (BNN). This approach is particularly beneficial in medical imaging, such as chest X-rays, where a diverse range of disease characteristics and severities must be represented despite the scarcity of labeled samples.

Overview of the Method

The proposed method integrates three key components:

1. Sample Generation: cGANs are used to produce synthetic chest X-ray images conditioned on a real sample. By altering projection masks, multiple images representative of varying clinical information are generated.
2. Classification/Segmentation Model: The framework leverages existing neural network models, such as VGG16 or ResNet18, utilizing an initial set of labeled images that is incrementally expanded as informative samples are added.
3. Sample Informativeness Calculation: A BNN is applied to evaluate the uncertainty associated with generated samples, guiding the selection of images that provide substantial learning information to the model.

Experimental Findings

The experimental setup involved customizing a training pipeline using chest X-ray datasets to evaluate classification and segmentation metrics (sensitivity, specificity, Dice Metric, Hausdorff Distance). Results indicated that the AL framework achieved optimal performance using only about 35% of the benchmark dataset, thus reducing computational and human annotation resources significantly. For instance, AL reached an AUC of 95.2% with VGG16 and 95.3% with ResNet18, which closely rivals the performance seen with full labeled datasets in fully supervised learning (FSL).

Implications and Future Work

The paper conveys meaningful implications for enhancing DL models' efficiency in medical imaging applications by optimizing data usage. This method reduces annotation efforts, making it feasible for practitioners with constrained resources. A noteworthy strength lies in generating clinically plausible image variations that enrich training datasets, thereby enabling robust model performance even with limited labeled data.

Moving forward, leveraging cGAN-generated synthetic data in other medical imaging domains could be explored, particularly where underrepresentation affects diagnostic accuracy. The use of AL frameworks like the one proposed could pave the way for real-time clinical decision support systems that maximize AI's potential even with suboptimal input data.

Conclusion

By dynamically prioritizing the most informative samples, this research delineates a practical pathway to achieve state-of-the-art classification and segmentation accuracy in medical image analysis. This paper underscores the value of incorporating generative models with uncertainty estimations into the DL training pipeline, amplifying expert workflows while enriching dataset quality. The approach holds promise for future AI developments in situations where labeled data acquisition is challenging, thereby promoting more rapid deployment of AI solutions in healthcare environments.