Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation

Understand

The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.

• Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets.
• However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire.
• Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations.