Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation (1706.04737v1)

Abstract: Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.

Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

The paper "Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation" by Lin Yang et al. discusses an innovative approach to reducing annotation costs in biomedical image segmentation. The researchers propose a deep active learning framework that judiciously selects instances for annotation, thereby optimizing the training data utilization for segmentation tasks.

Overview

Biomedical image segmentation has gained traction with the success of deep learning models. However, a significant limitation persists in acquiring sufficient annotated data due to the complexity and expert-level requirement for annotation. This research addresses the challenge of identifying which instances should be annotated to maximize the segmentation performance with minimal data.

Methodology

The proposed framework integrates Fully Convolutional Networks (FCN) and active learning to minimize annotation effort. The researchers leverage uncertainty and similarity metrics derived from FCNs to discern the most representative and uncertain areas needing annotation. They formulate this as a generalized maximum set cover problem, demonstrating an optimal balance between uncertainty reduction and representativeness in training data selection.

The core components of the framework include:

1. New FCN Architecture: The paper introduces an FCN model optimized for better performance and training speed. Techniques such as batch normalization and residual networks, alongside a bottleneck design, contribute to reduced parameters and enhanced generality, particularly when limited training data is available.
2. Uncertainty and Similarity Estimation: The framework estimates uncertainty using bootstrapping and employs cosine similarity for representativeness. These estimates guide the selection of the most valuable samples for training, balancing uncertainty and the coverage of diverse instances.
3. Annotation Suggestion Algorithm: Based on the uncertainty and similarity estimates, an algorithm suggests the most informative samples to annotate, effectively reducing redundant annotations and optimizing the data acquisition process.

Results

The framework was tested on datasets from the 2015 MICCAI Gland Challenge and lymph node ultrasound images. Using only 50% of the training data, the proposed method achieved state-of-the-art segmentation performance. Compared with random and purely uncertainty-based sampling methods, the suggestive annotation approach consistently yielded superior results.

Implications

This deep active learning framework holds significant implications for biomedical imaging, where data acquisition costs are high. By smartly reducing the amount of necessary annotated data, this research paves the way for efficient training of deep learning models in resource-constrained settings. Additionally, it demonstrates potential extensions to other domains where annotation is costly or labor-intensive.

Future Directions

The paper's insights open avenues for further exploration in active learning strategies, particularly in integrating more sophisticated metrics for data selection. Enhancing the generalizability of FCNs in diverse imaging modalities remains an area for continued research, alongside exploring hybrid models that combine active learning with semi-supervised approaches to further reduce annotation dependencies.

Overall, this work offers a notable contribution to optimizing deep learning applications in biomedical image analysis, demonstrating effective strategies for reducing the reliance on exhaustive manual annotations.