Artificial Intelligence for In Silico Clinical Trials: A Review

A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled in silico trials can increase the case group size by creating virtual cohorts as controls. In addition, it also enables automation and optimization of trial design and predicts the trial success rate. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design. We focus on how machine learning (ML) may be applied in these applications. In particular, we present the machine learning problem formulation and available data sources for each task. We end with discussing the challenges and opportunities of AI for in silico trials in real-world applications

Methods for data-related problems in person re-ID

In the last years, the ever-increasing need for public security has attracted wide attention in person re-ID. State-of-the-art techniques have achieved impressive results on academic datasets, which are nearly saturated. However, when it comes to deploying a re-ID system in a practical surveillance scenario, several challenges arise. 1) Full person views are often unavailable, and missing body parts make the comparison very challenging due to significant misalignment of the views. 2) Low diversity in training data introduces bias in re-ID systems. 3) The available data might come from different modalities, e.g., text and images. This thesis proposes Partial Matching Net (PMN) that detects body joints, aligns partial views, and hallucinates the missing parts based on the information present in the frame and a learned model of a person. The aligned and reconstructed views are then combined into a joint representation and used for matching images. The thesis also investigates different types of bias that typically occur in re-ID scenarios when the similarity between two persons is due to the same pose, body part, or camera view, rather than to the ID-related cues. It proposes a general approach to mitigate these effects named Bias-Control (BC) framework with two training streams leveraging adversarial and multitask learning to reduce bias-related features. Finally, the thesis investigates a novel mechanism for matching data across visual and text modalities. It proposes a framework Text (TAVD) with two complementary modules: Text attribute feature aggregation (TA) that aggregates multiple semantic attributes in a bimodal space for globally matching text descriptions with images and Visual feature decomposition (VD) which performs feature embedding for locally matching image regions with text attributes. The results and comparison to state of the art on different benchmarks show that the proposed solutions are effective strategies for person re-ID.Open Acces

Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions

Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions

Deep learning has seen rapid growth in recent years and achieved state-of-the-art performance in a wide range of applications. However, training models typically requires expensive and time-consuming collection of large quantities of labeled data. This is particularly true within the scope of medical imaging analysis (MIA), where data are limited and labels are expensive to be acquired. Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data. In this survey, we extensively investigated over 300 recent papers to provide a comprehensive overview of recent progress on label-efficient learning strategies in MIA. We first present the background of label-efficient learning and categorize the approaches into different schemes. Next, we examine the current state-of-the-art methods in detail through each scheme. Specifically, we provide an in-depth investigation, covering not only canonical semi-supervised, self-supervised, and multi-instance learning schemes, but also recently emerged active and annotation-efficient learning strategies. Moreover, as a comprehensive contribution to the field, this survey not only elucidates the commonalities and unique features of the surveyed methods but also presents a detailed analysis of the current challenges in the field and suggests potential avenues for future research.Comment: Update Few-shot Method

Deep Neural Networks and Tabular Data: Inference, Generation, and Explainability

Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods

Sparsity-aware neural user behavior modeling in online interaction platforms

Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences. Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data. Despite their enormous potential, we encounter the unique challenge of data sparsity for a vast majority of entities, e.g., sparsity in ground-truth labels for entities and in entity-level interactions (cold-start users, items in the long-tail, and ephemeral groups). In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications. Our problem settings span transductive and inductive learning scenarios, where transductive learning models entities seen during training and inductive learning targets entities that are only observed during inference. We leverage different facets of information reflecting user behavior (e.g., interconnectivity in social networks, temporal and attributed interaction information) to enable personalized inference at scale. Our proposed models are complementary to concurrent advances in neural architectural choices and are adaptive to the rapid addition of new applications in online platforms. First, we examine two transductive learning settings: inference and recommendation in graph-structured and bipartite user-item interactions. In chapter 3, we formulate user profiling in social platforms as semi-supervised learning over graphs given sparse ground-truth labels for node attributes. We present a graph neural network framework that exploits higher-order connectivity structures (network motifs) to learn attributed structural roles of nodes that identify structurally similar nodes with co-varying local attributes. In chapter 4, we design neural collaborative filtering models for few-shot recommendations over user-item interactions. To address item interaction sparsity due to heavy-tailed distributions, our proposed meta-learning framework learns-to-recommend few-shot items by knowledge transfer from arbitrary base recommenders. We show that our framework consistently outperforms state-of-art approaches on overall recommendation (by 5% Recall) while achieving significant gains (of 60-80% Recall) for tail items with fewer than 20 interactions. Next, we explored three inductive learning settings: modeling spread of user-generated content in social networks; item recommendations for ephemeral groups; and friend ranking in large-scale social platforms. In chapter 5, we focus on diffusion prediction in social networks where a vast population of users rarely post content. We introduce a deep generative modeling framework that models users as probability distributions in the latent space with variational priors parameterized by graph neural networks. Our approach enables massive performance gains (over 150% recall) for users with sparse activities while being faster than state-of-the-art neural models by an order of magnitude. In chapter 6, we examine item recommendations for ephemeral groups with limited or no historical interactions together. To overcome group interaction sparsity, we present self-supervised learning strategies that exploit the preference co-variance in observed group memberships for group recommender training. Our framework achieves significant performance gains (over 30% NDCG) over prior state-of-the-art group recommendation models. In chapter 7, we introduce multi-modal inference with graph neural networks that captures knowledge from multiple feature modalities and user interactions for multi-faceted friend ranking. Our approach achieves notable higher performance gains for critical populations of less-active and low degree users