Development Of Limited Supervised Deep Learning Methods For Biomedical Image Analysis

Abstract

Over the past few years, the computer vision domain has evolved and made a revo- lutionary transition from human-engineered features to automated features to address challenging tasks. Computer vision is an ever-evolving domain, having its roots deeply correlated with neuroscience. Any new findings that trigger a more intuitive under- standing and working of the human brain generally impact the design of computer vision algorithms. The convolutional neural network is one such algorithm that has become the de facto standard for most computer vision tasks, such as image classifi- cation, object detection, image segmentation, etc. However, the performance of CNNs is highly dependent on labeled data, making their practicability difficult in scenarios lacking sufficient labeled data, especially in medical applications. Therefore, it is im- perative to develop deep learning methods with limited supervision. In light of this, we explore the dimensions of deep learning with limited supervision through capsule networks and semi-supervised learning for biomedical image analysis, with a primary focus on segmentation. In this thesis, we have systematically reviewed various techniques for handling deep learning with limited labeled data, focusing on capsule networks and consis- tency regularization-driven semi-supervised learning. Capsule networks have shown immense potential for image classification tasks. However, extending it to pixel-level classification or segmentation is difficult. It poses numerous challenges, including the exponential growth of trainable parameters, expensive computation, and extensive memory overhead. In this regard, we propose DRIP-Caps, a Dilated Residual Incep- tion and Capsule Pooling framework that makes the capsule network lightweight by re- ducing the computation complexity without compromising performance on the CSCR (central Serous Chorioretinopathy) dataset. viiiSemi-supervised learning is a major discipline that alleviates the requirement for labeled data by incorporating labeled and unlabeled data to formulate pertinent infor- mation. We present a semi-supervised framework based on a mixup operation-driven consistency constraint for medical image segmentation by incorporating geometric con- straints regressing over the signed distance map (SDM) of the object of interest, achiev- ing superior performance on the publicly available ACDC and LA datasets. We also propose a novel semi-supervised framework for enforcing dual consistency (data level and network level) with the two-stage pre-training approach through networks of differ- ent learning paradigms enforcing both local and global semantic affinities, improving the overall performance. We envision these methods serving a major role in alleviating the tedious labeling process as far as the segmentation task is concerned.