With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. As a sub-domain, medication recommendation aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing works ignore the inherent long-tailed distribution of medical data, have uneven learning strengths for hot and sparse data, and fail to balance safety and accuracy. To address the above limitations, we propose StratMed, which introduces a stratification strategy that overcomes the long-tailed problem and achieves fuller learning of sparse data. It also utilizes a dual-property network to address the issue of mutual constraints on the safety and accuracy of medication combinations, synergistically enhancing these two properties. Specifically, we construct a pre-training method using deep learning networks to obtain medication and disease representations. After that, we design a pyramid-like stratification method based on relevance to strengthen the expressiveness of sparse data. Based on this relevance, we design two graph structures to express medication safety and precision at the same level to obtain patient representations. Finally, the patient's historical clinical information is fitted to generate medication combinations for the current health condition. We employed the MIMIC-III dataset to evaluate our model against state-of-the-art methods in three aspects comprehensively. Compared to the sub-optimal baseline model, our model reduces safety risk by 15.08\%, improves accuracy by 0.36\%, and reduces training time consumption by 81.66\%.
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To enable machines to learn how humans interact with the physical world in our daily activities, it is crucial to provide rich data that encompasses the 3D motion of humans as well as the motion of objects in a learnable 3D representation. Ideally, this data should be collected in a natural setup, capturing the authentic dynamic 3D signals during human-object interactions. To address this challenge, we introduce the ParaHome system, designed to capture and parameterize dynamic 3D movements of humans and objects within a common home environment. Our system consists of a multi-view setup with 70 synchronized RGB cameras, as well as wearable motion capture devices equipped with an IMU-based body suit and hand motion capture gloves. By leveraging the ParaHome system, we collect a novel large-scale dataset of human-object interaction. Notably, our dataset offers key advancement over existing datasets in three main aspects: (1) capturing 3D body and dexterous hand manipulation motion alongside 3D object movement within a contextual home environment during natural activities; (2) encompassing human interaction with multiple objects in various episodic scenarios with corresponding descriptions in texts; (3) including articulated objects with multiple parts expressed with parameterized articulations. Building upon our dataset, we introduce new research tasks aimed at building a generative model for learning and synthesizing human-object interactions in a real-world room setting.
Accurate grasping is the key to several robotic tasks including assembly and household robotics. Executing a successful grasp in a cluttered environment requires multiple levels of scene understanding: First, the robot needs to analyze the geometric properties of individual objects to find feasible grasps. These grasps need to be compliant with the local object geometry. Second, for each proposed grasp, the robot needs to reason about the interactions with other objects in the scene. Finally, the robot must compute a collision-free grasp trajectory while taking into account the geometry of the target object. Most grasp detection algorithms directly predict grasp poses in a monolithic fashion, which does not capture the composability of the environment. In this paper, we introduce an end-to-end architecture for object-centric grasping. The method uses pointcloud data from a single arbitrary viewing direction as an input and generates an instance-centric representation for each partially observed object in the scene. This representation is further used for object reconstruction and grasp detection in cluttered table-top scenes. We show the effectiveness of the proposed method by extensively evaluating it against state-of-the-art methods on synthetic datasets, indicating superior performance for grasping and reconstruction. Additionally, we demonstrate real-world applicability by decluttering scenes with varying numbers of objects.
Patients derive numerous benefits from reading their clinical notes, including an increased sense of control over their health and improved understanding of their care plan. However, complex medical concepts and jargon within clinical notes hinder patient comprehension and may lead to anxiety. We developed a patient-facing tool to make clinical notes more readable, leveraging large language models (LLMs) to simplify, extract information from, and add context to notes. We prompt engineered GPT-4 to perform these augmentation tasks on real clinical notes donated by breast cancer survivors and synthetic notes generated by a clinician, a total of 12 notes with 3868 words. In June 2023, 200 female-identifying US-based participants were randomly assigned three clinical notes with varying levels of augmentations using our tool. Participants answered questions about each note, evaluating their understanding of follow-up actions and self-reported confidence. We found that augmentations were associated with a significant increase in action understanding score (0.63 $\pm$ 0.04 for select augmentations, compared to 0.54 $\pm$ 0.02 for the control) with p=0.002. In-depth interviews of self-identifying breast cancer patients (N=7) were also conducted via video conferencing. Augmentations, especially definitions, elicited positive responses among the seven participants, with some concerns about relying on LLMs. Augmentations were evaluated for errors by clinicians, and we found misleading errors occur, with errors more common in real donated notes than synthetic notes, illustrating the importance of carefully written clinical notes. Augmentations improve some but not all readability metrics. This work demonstrates the potential of LLMs to improve patients' experience with clinical notes at a lower burden to clinicians. However, having a human in the loop is important to correct potential model errors.
Training models with robust group fairness properties is crucial in ethically sensitive application areas such as medical diagnosis. Despite the growing body of work aiming to minimise demographic bias in AI, this problem remains challenging. A key reason for this challenge is the fairness generalisation gap: High-capacity deep learning models can fit all training data nearly perfectly, and thus also exhibit perfect fairness during training. In this case, bias emerges only during testing when generalisation performance differs across subgroups. This motivates us to take a bi-level optimisation perspective on fair learning: Optimising the learning strategy based on validation fairness. Specifically, we consider the highly effective workflow of adapting pre-trained models to downstream medical imaging tasks using parameter-efficient fine-tuning (PEFT) techniques. There is a trade-off between updating more parameters, enabling a better fit to the task of interest vs. fewer parameters, potentially reducing the generalisation gap. To manage this tradeoff, we propose FairTune, a framework to optimise the choice of PEFT parameters with respect to fairness. We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets. The code is available at //github.com/Raman1121/FairTune
In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.
Serverless computing relieves developers from the burden of resource management, thus providing ease-of-use to the users and the opportunity to optimize resource utilization for the providers. However, today's serverless systems lack performance guarantees for function invocations, thus limiting support for performance-critical applications: we observed severe performance variability (up to 6x). Providers lack visibility into user functions and hence find it challenging to right-size them: we observed heavy resource underutilization (up to 80%). To understand the causes behind the performance variability and underutilization, we conducted a measurement study of commonly deployed serverless functions and learned that the function performance and resource utilization depend crucially on function semantics and inputs. Our key insight is to delay making resource allocation decisions until after the function inputs are available. We introduce Shabari, a resource management framework for serverless systems that makes decisions as late as possible to right-size each invocation to meet functions' performance objectives (SLOs) and improve resource utilization. Shabari uses an online learning agent to right-size each function invocation based on the features of the function input and makes cold-start-aware scheduling decisions. For a range of serverless functions and inputs, Shabari reduces SLO violations by 11-73% while not wasting any vCPUs and reducing wasted memory by 64-94% in the median case, compared to state-of-the-art systems, including Aquatope, Parrotfish, and Cypress.
Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review could inspire the research community to explore solutions for this challenge and further promote the developments in medical image segmentation field.
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Despite these concerns and risks, there are currently no concrete guidelines and best practices for guiding future AI developments in medical imaging towards increased trust, safety and adoption. To bridge this gap, this paper introduces a careful selection of guiding principles drawn from the accumulated experiences, consensus, and best practices from five large European projects on AI in Health Imaging. These guiding principles are named FUTURE-AI and its building blocks consist of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability. In a step-by-step approach, these guidelines are further translated into a framework of concrete recommendations for specifying, developing, evaluating, and deploying technically, clinically and ethically trustworthy AI solutions into clinical practice.
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.
Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
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