Generalizable deep learning based medical image segmentation

Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications. To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques. In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain. For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios. In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation. In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method. Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces

Fair representations in the data domain

Algorithmic fairness is a multi-faceted topic which is of significant consequence to a diverse range of people. The issue that this thesis investigates is a fairness-specific instance of a yet even broader concern — that data can be biased due to spurious correlations. Machine learning models trained on such data learn to exploit these spurious correlations that do not hold in the test distribution. When spurious correlations are found with respect to protected demographic attributes, trained models could be biased towards certain subgroups or populations. A promising approach to counteract biased data is by producing a fair representation as a pre-processing step. The main drawback, however, of existing fair representation learning approaches is that the data often become obscured when projected into an uninterpretable latent space, making intuitive assessment difficult. Noticing that the domain the data resides in is often interpretable, with the structure providing richer information that is easier to understand on a per sample basis, I develop fair representations in the data domain. These convey additional per-sample information that can be easily shared and explained to system designers and stakeholders. This thesis investigates three aspects of fair representations in the data domain. Firstly, I demonstrate a novel application of fair representations to generate counterfactual samples in the data domain. The aim of this application is to promote positive actions to address discrimination in an already existing system; Secondly, I develop a method to produce fair representations in the data domain based on statistical dependence principles; Lastly, I take this approach further, introducing two further methods to achieve fair representations in the data domain based on adversarial learning

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

BALANCING THE ASSUMPTIONS OF CAUSAL INFERENCE AND NATURAL LANGUAGE PROCESSING

Drawing conclusions about real-world relationships of cause and effect from data collected without randomization requires making assumptions about the true processes that generate the data we observe. Causal inference typically considers low-dimensional data such as categorical or numerical fields in structured medical records. Yet a restriction to such data excludes natural language texts -- including social media posts or clinical free-text notes -- that can provide a powerful perspective into many aspects of our lives. This thesis explores whether the simplifying assumptions we make in order to model human language and behavior can support the causal conclusions that are necessary to inform decisions in healthcare or public policy. An analysis of millions of documents must rely on automated methods from machine learning and natural language processing, yet trust is essential in many clinical or policy applications. We need to develop causal methods that can reflect the uncertainty of imperfect predictive models to inform robust decision-making. We explore several areas of research in pursuit of these goals. We propose a measurement error approach for incorporating text classifiers into causal analyses and demonstrate the assumption on which it relies. We introduce a framework for generating synthetic text datasets on which causal inference methods can be evaluated, and use it to demonstrate that many existing approaches make assumptions that are likely violated. We then propose a proxy model methodology that provides explanations for uninterpretable black-box models, and close by incorporating it into our measurement error approach to explore the assumptions necessary for an analysis of gender and toxicity on Twitter

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning

Sociolinguistically Driven Approaches for Just Natural Language Processing

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying disparities in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxonomy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work