30 research outputs found
Contrastive Attention for Automatic Chest X-ray Report Generation
Recently, chest X-ray report generation, which aims to automatically generate
descriptions of given chest X-ray images, has received growing research
interests. The key challenge of chest X-ray report generation is to accurately
capture and describe the abnormal regions. In most cases, the normal regions
dominate the entire chest X-ray image, and the corresponding descriptions of
these normal regions dominate the final report. Due to such data bias,
learning-based models may fail to attend to abnormal regions. In this work, to
effectively capture and describe abnormal regions, we propose the Contrastive
Attention (CA) model. Instead of solely focusing on the current input image,
the CA model compares the current input image with normal images to distill the
contrastive information. The acquired contrastive information can better
represent the visual features of abnormal regions. According to the experiments
on the public IU-X-ray and MIMIC-CXR datasets, incorporating our CA into
several existing models can boost their performance across most metrics. In
addition, according to the analysis, the CA model can help existing models
better attend to the abnormal regions and provide more accurate descriptions
which are crucial for an interpretable diagnosis. Specifically, we achieve the
state-of-the-art results on the two public datasets.Comment: Appear in Findings of ACL 2021 (The Joint Conference of the 59th
Annual Meeting of the Association for Computational Linguistics and the 11th
International Joint Conference on Natural Language Processing (ACL-IJCNLP
2021)
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Today's AI systems for medical decision support often succeed on benchmark
datasets in research papers but fail in real-world deployment. This work
focuses on the decision making of sepsis, an acute life-threatening systematic
infection that requires an early diagnosis with high uncertainty from the
clinician. Our aim is to explore the design requirements for AI systems that
can support clinical experts in making better decisions for the early diagnosis
of sepsis. The study begins with a formative study investigating why clinical
experts abandon an existing AI-powered Sepsis predictive module in their
electrical health record (EHR) system. We argue that a human-centered AI system
needs to support human experts in the intermediate stages of a medical
decision-making process (e.g., generating hypotheses or gathering data),
instead of focusing only on the final decision. Therefore, we build SepsisLab
based on a state-of-the-art AI algorithm and extend it to predict the future
projection of sepsis development, visualize the prediction uncertainty, and
propose actionable suggestions (i.e., which additional laboratory tests can be
collected) to reduce such uncertainty. Through heuristic evaluation with six
clinicians using our prototype system, we demonstrate that SepsisLab enables a
promising human-AI collaboration paradigm for the future of AI-assisted sepsis
diagnosis and other high-stakes medical decision making.Comment: Under submission to CHI202
Peregrine and saker falcon genome sequences provide insights into evolution of a predatory lifestyle
As top predators, falcons possess unique morphological, physiological and behavioral adaptations that allow them to be successful hunters: for example, the peregrine is renowned as the world's fastest animal. To examine the evolutionary basis of predatory adaptations, we sequenced the genomes of both the peregrine (Falco peregrinus) and saker falcon (Falco cherrug), and we present parallel, genome-wide evidence for evolutionary innovation and selection for a predatory lifestyle. The genomes, assembled using Illumina deep sequencing with greater than 100-fold coverage, are both approximately 1.2 Gb in length, with transcriptome-assisted prediction of approximately 16,200 genes for both species. Analysis of 8,424 orthologs in both falcons, chicken, zebra finch and turkey identified consistent evidence for genome-wide rapid evolution in these raptors. SNP-based inference showed contrasting recent demographic trajectories for the two falcons, and gene-based analysis highlighted falcon-specific evolutionary novelties for beak development and olfaction and specifically for homeostasis-related genes in the arid environment–adapted saker
Multimodal risk prediction with physiological signals, medical images and clinical notes
The broad adoption of electronic health record (EHR) systems brings us a tremendous amount of clinical data and thus provides opportunities to conduct data-based healthcare research to solve various clinical problems in the medical domain. Machine learning and deep learning methods are widely used in the medical informatics and healthcare domain due to their power to mine insights from raw data. When adapting deep learning models for EHR data, it is essential to consider its heterogeneous nature: EHR contains patient records from various sources including medical tests (e.g. blood test, microbiology test), medical imaging, diagnosis, medications, procedures, clinical notes, etc. Those modalities together provide a holistic view of patient health status and complement each other. Therefore, combining data from multiple modalities that are intrinsically different is challenging but intuitively promising in deep learning for EHR. To assess the expectations of multimodal data, we introduce a comprehensive fusion framework designed to integrate temporal variables, medical images, and clinical notes in EHR for enhanced performance in clinical risk prediction. Early, joint, and late fusion strategies are employed to combine data from various modalities effectively. We test the model with three predictive tasks: in-hospital mortality, long length of stay, and 30-day readmission. Experimental results show that multimodal models outperform uni-modal models in the tasks involved. Additionally, by training models with different input modality combinations, we calculate the Shapley value for each modality to quantify their contribution to multimodal performance. It is shown that temporal variables tend to be more helpful than CXR images and clinical notes in the three explored predictive tasks
Identification and Classification of Atmospheric Particles Based on SEM Images Using Convolutional Neural Network with Attention Mechanism
Accurate identification and classification of atmospheric particulates can provide the basis for their source apportionment. Most current research studies mainly focus on the classification of atmospheric particles based on the energy spectrum of particles, which has the problems of low accuracy and being time-consuming. It is necessary to study the classification method of atmospheric particles with higher accuracy. In this paper, a convolutional neural network (CNN) model with attention mechanism is proposed to identify and classify the scanning electron microscopy (SEM) images of atmospheric particles. First, this work established a database, Qingdao 2016–2018, for atmospheric particles classification research. This database consists of 3469 SEM images of single particulates. Secondly, by analyzing the morphological characteristics of single particle SEM images, it can be divided into four categories: fibrous particles, flocculent particles, spherical particles, and mineral particles. Thirdly, by introducing attention mechanism into convolutional neural network, an Attention-CNN model for the identification and classification of the four types of atmospheric particles based on the SEM images is established. Finally, the Attention-CNN model is trained and tested based on the SEM images database, and the results of identification and classification for four types of particles are obtained. Under the same SEM images database, the classification results from Attention-CNN are compared with those of CNN and SVM. It is found that Attention-CNN has higher classification accuracy and reduces significantly the misclassification number of particles, which shows the focusing effect of attention mechanism
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes
Despite intense efforts in basic and clinical research, an individualized
ventilation strategy for critically ill patients remains a major challenge.
Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on
electronic health records (EHR) has attracted interest from both the healthcare
industry and machine learning research community. However, most learned DTR
policies might be biased due to the existence of confounders. Although some
treatment actions non-survivors received may be helpful, if confounders cause
the mortality, the training of RL models guided by long-term outcomes (e.g.,
90-day mortality) would punish those treatment actions causing the learned DTR
policies to be suboptimal. In this study, we develop a new deconfounding
actor-critic network (DAC) to learn optimal DTR policies for patients. To
alleviate confounding issues, we incorporate a patient resampling module and a
confounding balance module into our actor-critic framework. To avoid punishing
the effective treatment actions non-survivors received, we design a short-term
reward to capture patients' immediate health state changes. Combining
short-term with long-term rewards could further improve the model performance.
Moreover, we introduce a policy adaptation method to successfully transfer the
learned model to new-source small-scale datasets. The experimental results on
one semi-synthetic and two different real-world datasets show the proposed
model outperforms the state-of-the-art models. The proposed model provides
individualized treatment decisions for mechanical ventilation that could
improve patient outcomes