Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors

Deep Multi-View Learning for Visual Understanding

PhD ThesisMulti-view data is the result of an entity being perceived or represented from multiple perspectives. Plenty of applications in visual understanding contain multi-view data. For example, the face images for training a recognition system are usually captured by different devices from multiple angles. This thesis focuses on the cross-view visual recognition problems, e.g., identifying the face images of the same person across different cameras. Several representative multi-view settings, from the supervised multi-view learning to the more challenging unsupervised domain adaptive (UDA) multi-view learning, are investigated. Novel multi-view learning algorithms are proposed correspondingly. To be more specific, the proposed methods are based on the advanced deep neural network (DNN) architectures for better handling visual data. However, directly combining the multi-view learning objectives with DNN can result in different issues, e.g., on scalability, and limit the application scenarios and model performance. Corresponding novelties in DNN methods are thus required to solve them. This thesis is organised into three parts. Each chapter focuses on a multi-view learning setting with novel solutions and is detailed as follows: Chapter 3 A supervised multi-view learning setting with two different views are studied. To recognise the data samples across views, one strategy is aligning them in a common feature space via correlation maximisation. It is also known as canonical correlation analysis (CCA). Deep CCA has been proposed for better performance with the non-linear projection via deep neural networks. Existing deep CCA models typically decorrelate the deep feature dimensions of each view before their Euclidean distances are minimised in the common space. This feature decorrelation is achieved by enforcing an exact decorrelation constraint which is computationally expensive due to the matrix inversion or SVD operations. Therefore, existing deep CCA models are inefficient and have scalability issues. Furthermore, the exact decorrelation is incompatible with the gradient based deep model training and results in sub-optimal solution. To overcome these aforementioned issues, a novel deep CCA model Soft CCA is introduced in this thesis. Specifically, the exact decorrelation is replaced by soft decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL). It can be jointly optimised with the other training objectives. In addition, our SDL loss can be applied to other deep models beyond multi-view learning. Chapter 4 The supervised multi-view learning setting, whereby more than two views exist, are studied in this chapter. Recently developed deep multi-view learning algorithms either learn a latent visual representation based on a single semantic level and/or require laborious human annotation of these factors as attributes. A novel deep neural network architecture, called Multi- Level Factorisation Net (MLFN), is proposed to automatically factorise the visual appearance into latent discriminative factors at multiple semantic levels without manual annotation. The main purpose is forcing different views share the same latent factors so that they are can be aligned at all layers. Specifically, MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned feature, and they can be fused efficiently. The effectiveness of the proposed MLFN is demonstrated by not only the large-scale cross-view recognition problems but also the general object categorisation tasks. Chapter 5 The last problem is a special unsupervised domain adaptation setting called unsupervised domain adaptive (UDA) multi-view learning. It contains a fully annotated dataset as the source domain and another unsupervised dataset with relevant tasks as the target domain. The main purpose is to improve the performance of the unlabelled dataset with the annotated data from the other dataset. More importantly, this setting further requires both the source and target domains are multi-view datasets with relevant tasks. Therefore, the assumption of the aligned label space across domains is inappropriate in the UDA multi-view learning. For example, the person re-identification (Re-ID) datasets built on different surveillance scenarios are with images of different people captured and should be given disjoint person identity labels. Existing methods for UDA multi-view learning problems are aligning different domains either in the raw image space or a feature embedding space for domain alignment. In this thesis, a different framework, multi-task learning, is adopted with the domain specific objectives for a common space learning. Specifically, such common space is proposed to enable the knowledge transfer. The conventional supervised losses can be used for the labelled source data while the unsupervised objectives for the target domain play the key roles in domain adaptation. Two novel unsupervised objectives are introduced for UDA multi-view learning and result in two models as below. The first model, termed common factorised space model (CFSM), is built on the assumptions that the semantic latent attributes are shared between the source and target domains since they are relevant multi-view learning tasks. Different from the existing methods that based on domain alignment, CFSM emphasizes on transferring the information across domains via discovering discriminative latent factors in the proposed common space. However, the multi-view data from target domain is without labels. Therefore, an unsupervised factorisation loss is derived and applied on the common space for latent factors discovery across domains. The second model still learns a shared embedding space with multi-view data from both domains but with a different assumption. It attempts to discover the latent correspondence of multi-view data in the unsupervised target data. The target data’s contribution comes from a clustering process. Each cluster thus reveals the underlying cross-view correspondences across multiple views in target domain. To this end, a novel Stochastic Inference for Deep Clustering (SIDC) method is proposed. It reduces self-reinforcing errors that lead to premature convergence to a sub-optimal solution by changing the conventional deterministic cluster assignment to a stochastic one

MMFL-Net: Multi-scale and Multi-granularity Feature Learning for Cross-domain Fashion Retrieval

Instance-level image retrieval in fashion is a challenging issue owing to its increasing importance in real-scenario visual fashion search. Cross-domain fashion retrieval aims to match the unconstrained customer images as queries for photographs provided by retailers; however, it is a difficult task due to a wide range of consumer-to-shop (C2S) domain discrepancies and also considering that clothing image is vulnerable to various non-rigid deformations. To this end, we propose a novel multi-scale and multi-granularity feature learning network (MMFL-Net), which can jointly learn global-local aggregation feature representations of clothing images in a unified framework, aiming to train a cross-domain model for C2S fashion visual similarity. First, a new semantic-spatial feature fusion part is designed to bridge the semantic-spatial gap by applying top-down and bottom-up bidirectional multi-scale feature fusion. Next, a multi-branch deep network architecture is introduced to capture global salient, part-informed, and local detailed information, and extracting robust and discrimination feature embedding by integrating the similarity learning of coarse-to-fine embedding with the multiple granularities. Finally, the improved trihard loss, center loss, and multi-task classification loss are adopted for our MMFL-Net, which can jointly optimize intra-class and inter-class distance and thus explicitly improve intra-class compactness and inter-class discriminability between its visual representations for feature learning. Furthermore, our proposed model also combines the multi-task attribute recognition and classification module with multi-label semantic attributes and product ID labels. Experimental results demonstrate that our proposed MMFL-Net achieves significant improvement over the state-of-the-art methods on the two datasets, DeepFashion-C2S and Street2Shop.Comment: 27 pages, 12 figures, Published by <Multimedia Tools and Applications

Modeling of mutual dependencies

Data analysis means applying computational models to analyzing large collections of data, such as video signals, text collections, or measurements of gene activities in human cells. Unsupervised or exploratory data analysis refers to a subtask of data analysis, in which the goal is to find novel knowledge based on only the data. A central challenge in unsupervised data analysis is separating relevant and irrelevant information from each other. In this thesis, novel solutions to focusing on more relevant findings are presented. Measurement noise is one source of irrelevant information. If we have several measurements of the same objects, the noise can be suppressed by averaging over the measurements. Simple averaging is, however, only possible when the measurements share a common representation. In this thesis, we show how irrelevant information can be suppressed or ignored also in cases where the measurements come from different kinds of sensors or sources, such as video and audio recordings of the same scene. For combining the measurements, we use mutual dependencies between them. Measures of dependency, such as mutual information, characterize commonalities between two sets of measurements. Two measurements can hence be combined to reduce irrelevant variation by finding new representations for the objects so that the representations are maximally dependent. The combination is optimal, given the assumption that what is in common between the measurements is more relevant than information specific to any one of the sources. Several practical models for the task are introduced. In particular, novel Bayesian generative models, including a Bayesian version of the classical method of canonical correlation analysis, are given. Bayesian modeling is especially justified approach to learning from small data sets. Hence, generative models can be used to extract dependencies in a more reliable manner in, for example, medical applications, where obtaining a large number of samples is difficult. Also, novel non-Bayesian models are presented: Dependent component analysis finds linear projections which capture more general dependencies than earlier methods. Mutual dependencies can also be used for supervising traditional unsupervised learning methods. The learning metrics principle describes how a new distance metric focusing on relevant information can be derived based on the dependency between the measurements and a supervising signal. In this thesis, the approximations and optimization methods required for using the learning metrics principle are improved

Multi-Modality Human Action Recognition

Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model

Cross-View Learning

PhDKey to achieving more efficient machine intelligence is the capability to analysing and understanding data across different views – which can be camera views or modality views (such as visual and textual). One generic learning paradigm for automated understanding data from different views called cross-view learning which includes cross-view matching, cross-view fusion and cross-view generation. Specifically, this thesis investigates two of them, cross-view matching and cross-view generation, by developing new methods for addressing the following specific computer vision problems. The first problem is cross-view matching for person re-identification which a person is captured by multiple non-overlapping camera views, the objective is to match him/her across views among a large number of imposters. Typically a person’s appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training samples. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power for cross-view matching. To that end, this thesis proposes to overcome the SSS problem in subspace learning by matching cross-view data in a discriminative null space of the training data. The second problem is cross-view matching for zero-shot learning where data are drawn from different modalities each for a different view (e.g. visual or textual), versus single-modal data considered in the first problem. This is inherently more challenging as the gap between different views becomes larger. Specifically, the zero-shot learning problem can be solved if the visual representation/view of the data (object) and its textual view are matched. Moreover, it requires learning a joint embedding space where different view data can be projected to for nearest neighbour search. This thesis argues that the key to make zero-shot learning models succeed is to choose the right embedding space. Different from most existing zero-shot learning models utilising a textual or an intermediate space as the embedding space for achieving crossview matching, the proposed method uniquely explores the visual space as the embedding space. This thesis finds that in the visual space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. Moreover, a natural mechanism for multiple textual modalities optimised jointly in an end-to-end manner in this model demonstrates significant advantages over existing methods. The last problem is cross-view generation for image captioning which aims to automatically generate textual sentences from visual images. Most existing image captioning studies are limited to investigate variants of deep learning-based image encoders, improving the inputs for the subsequent deep sentence decoders. Existing methods have two limitations: (i) They are trained to maximise the likelihood of each ground-truth word given the previous ground-truth words and the image, termed Teacher-Forcing. This strategy may cause a mismatch between training and testing since at test-time the model uses the previously generated words from the model distribution to predict the next word. This exposure bias can result in error accumulation in sentence generation during test time, since the model has never been exposed to its own predictions. (ii) The training supervision metric, such as the widely used cross entropy loss, is different from the evaluation metrics at test time. In other words, the model is not directly optimised towards the task expectation. This learned model is therefore suboptimal. One main underlying reason responsible is that the evaluation metrics are non-differentiable and therefore much harder to be optimised against. This thesis overcomes the problems as above by exploring the reinforcement learning idea. Specifically, a novel actor-critic based learning approach is formulated to directly maximise the reward - the actual Natural Language Processing quality metrics of interest. As compared to existing reinforcement learning based captioning models, the new method has the unique advantage of a per-token advantage and value computation is enabled leading to better model training