Massachusetts General Hospital（麻省总医院）；Polytechnique Montreal（蒙特利尔理工学院）；Mila, Quebec AI Institute（Mila魁北克人工智能研究所）；Massachusetts Institute of Technology（麻省理工学院）；MGH and BWH Center for Clinical Data Science（麻省总医院与布莱根妇女医院临床数据科学中心）；National Cancer Institute（美国国家癌症研究所）；Oregon Health and Science University（俄勒冈健康与科学大学）
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer
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Machine Learning for Health (ML4H) at NeurIPS 2021 - Extended Abstract
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Among the main features of robustness is repeatability. Much attention is given to classification performance without assessing the model repeatability, leading to the development of models that turn out to be unusable in practice. In this work, we evaluate the repeatability of four model types on images from the same patient that were acquired during the same visit. We study the performance of binary, multi-class, ordinal, and regression models on three medical image analysis tasks: cervical cancer screening, breast density estimation, and retinopathy of prematurity classification. Moreover, we assess the impact of sampling Monte Carlo dropout predictions at test time on classification performance and repeatability. Leveraging Monte Carlo predictions significantly increased repeatability for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 17% points.
Martinos Center for Biomedical Imaging（马蒂诺斯生物医学成像中心）；NeuroPoly, Polytechnique Montreal（蒙特利尔理工学院神经聚合物实验室）；Massachusetts Institute of Technology（麻省理工学院）；MGH & BWH Center for Clinical Data Science（麻省总医院与布莱根妇女医院临床数据科学中心）；University of Washington School of Public Health（华盛顿大学公共卫生学院）；National Cancer Institute（美国国家癌症研究所）；Oregon Health and Science University（俄勒冈健康与科学大学）
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Repeatable models output predictions with low variation during independent tests carried out under similar conditions. During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice. In this work, we evaluate the repeatability of four model types (binary classification, multi-class class…
Technical University of Berlin, Germany（柏林工业大学（德国））；Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany（波茨坦大学哈索普拉特纳数字工程研究所（德国））；Hasso Plattner Institute for Digital Health at the Icahn School of Medicine at Mount Sinai, NYC, USA（西奈山伊坎医学院哈索普拉特纳数字健康研究所（美国纽约市））
Self-supervised learning methods can be used to learn meaningful representations from unlabeled data that can be transferred to supervised downstream tasks to reduce the need for labeled data. In this paper, we propose a 3D self-supervised method that is based on the contrastive (SimCLR) method. Additionally, we show that employing Bayesian neural networks (with Monte-Carlo Dropout) during the inference phase can further enhance the results on the downstream tasks. We showcase our models on two medical imaging segmentation tasks: i) Brain Tumor Segmentation from 3D MRI, ii) Pancreas Tumor Segm…
Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout
Technical University of Munich（慕尼黑工业大学）；University of Oxford（牛津大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Tianjin University（天津大学）
针对深度学习定量 MRI 的精度与不确定性问题，提出训练和推理均使用 Monte Carlo dropout 并平均多次预测的方法，在小数据集下显著提升 FA、MD 图精度，还能生成辅助诊断的置信图。
Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
Department of Computer Engineering, Kwame Nkrumah University of Science and Technology（计算机工程系，加纳科拉布大学科学与技术大学）；Global Health and Infectious Disease Group, Kumasi Centre for Collaborative Research in Tropical Medicine（全球卫生与传染病组，科拉布热带医学协作研究中心）；Department of Radiology, National Hospital Abuja（放射科，阿布贾国家医院）；Computer Science Department, FAST National University of Computer and Emerging Sciences（计算机科学系，FAST国家计算机与新兴科学大学）；Medical Artificial Intelligence Lab, Lagos, Nigeria（医学人工智能实验室，尼日利亚拉各斯）；Department of Biomedical Engineering, McGill University（生物医学工程系，麦吉尔大学）；Perelman School of Medicine, University of Pennsylvania（佩尔曼医学学院，宾夕法尼亚大学）；Nnamdi Azikiwe University Teaching Hospital（纳曼迪·阿齐基韦大学教学医院）
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区生物医学工程学院）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；Inselspital, University of Bern（伯尔尼大学因塞尔医院）；Technical University of Munich（慕尼黑工业大学）
Tampere University（坦佩雷大学）；Finnish Environment Institute（芬兰环境研究所）；Aarhus University（奥胡斯大学）；XIAOMI（小米）
Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore the original colors of the objects. Recently, several deep learning-based approaches have been proposed to solve this problem and they often led to state-of-the-art performance in terms of average errors. However, for extreme samples, these methods fail and lead to high errors. In this paper, we address this limitation by proposing to aggregate different deep learning methods according to their output uncertainty. W…
Technion – Israel Institute of Technology（以色列理工学院）
Uncertainty quantification in deep-learning (DL) based image reconstruction models is critical for reliable clinical decision making based on the reconstructed images. We introduce "NPB-REC", a non-parametric fully Bayesian framework for uncertainty assessment in MRI reconstruction from undersampled "k-space" data. We use Stochastic gradient Langevin dynamics (SGLD) during the training phase to characterize the posterior distribution of the network weights. We demonstrated the added-value of our approach on the multi-coil brain MRI dataset, from the fastmri challenge, in comparison to the base…
The memorization effect of deep learning hinders its performance to effectively generalize on test set when learning with noisy labels. Prior study has discovered that epistemic uncertainty techniques are robust when trained with noisy labels compared with neural networks without uncertainty estimation. They obtain prolonged memorization effect and better generalization performance under the adversarial setting of noisy labels. Due to its superior performance amongst other selected epistemic uncertainty methods under noisy labels, we focus on Monte Carlo Dropout (MCDropout) and investigate why…
Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Moreover, producing deterministic outputs hinders DL adoption into clinical routines. Uncertainty estimates for the predictions would permit subsequent revision by clinicians. We …
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Erasmus MC, University Medical Center Rotterdam（鹿特丹大学医学中心伊拉斯谟MC）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学医学中心阿姆斯特丹UMC）；GE HealthCare（GE医疗）；Erasmus MC Cancer Institute（伊拉斯谟MC癌症研究所）；Medical Delta（医疗三角洲）
University College London（伦敦大学学院）；King’s College London（伦敦国王学院）；University of Electronic Science and Technology of China（电子科技大学）；University Hospitals Leuven（鲁汶大学医院）；Institute for Women’s Health（妇女健康研究所）
Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these different types of uncertainties for CNN-based 2D and 3D medical image segmentation tasks. We additionally propose a test-time augmentation-based aleatoric uncertainty to analyze the effect of different transformations of the input image on the segmentation output. Test-time augmentation has been previo…
Rethinking Inter-LoRA Orthogonality in Adapter Merging: Insights from Orthogonal Monte Carlo Dropout
University of Manchester（曼彻斯特大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；InstantX Team（InstantX团队）；University of Edinburgh（爱丁堡大学）；Aalto University（阿alto大学）
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所（IISRI））；Harvard Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学保尔森工程与应用科学学院）；Sharif University of Technology（谢里夫理工大学）；King’s College London（伦敦国王学院）；University of Waterloo（滑铁卢大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Dibrugarh University（迪布鲁格尔大学）；Ngee Ann Polytechnic（义安理工学院）
The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature…
Deep neural networks have been widely used for feature learning in facial expression recognition systems. However, small datasets and large intra-class variability can lead to overfitting. In this paper, we propose a method which learns an optimized compact network topology for real-time facial expression recognition utilizing localized facial landmark features. Our method employs a spatio-temporal bilinear layer as backbone to capture the motion of facial landmarks during the execution of a facial expression effectively. Besides, it takes advantage of Monte Carlo Dropout to capture the model'…
Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinicians is not whether the model is accurate, but whether the model can express to a human operator when it does not know if its answer is correct. We propose to use Monte Carlo dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning models to produce uncertainty estimations for radiation therapy dose prediction. We show that…
Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification
Institute for Intelligent Systems Research and Innovation (IISRI)（智能系统研究与创新研究所）；Deakin University（德金大学）；Concordia Institute for Information Systems Engineering（Concordia信息系统工程研究所）；Concordia University（Concordia大学）；The Graduate School of Biomedical Engineering（生物医学工程研究生院）；UNSW Sydney（新南威尔士大学悉尼分校）
Uncertainty Quantification In Surface Landmines and UXO Classification Using MC Dropout
Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science（罗切斯特理工学院，切斯特·F·卡森成像科学中心）；Jadavpur University, Department of Computer Science（贾梵布尔大学，计算机科学系）
LMU München（慕尼黑大学）；Technische Universität München（慕尼黑工业大学）；German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心）；Johns Hopkins University（约翰斯·霍普金斯大学）
We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty. Monte Carlo samples from the posterior distribution are efficiently generated using dropout at test time. Based on these samples, we introduce next to a voxel-wise uncertainty map also three metrics for structure-wise uncertainty. We then incorporate these structure-wise uncertainty in group analyses as a measure of confidence in the observation. Our results show that the metrics are highly correlated to segmentation accuracy and th…
Deep neural networks are becoming increasingly powerful and large and always require more labelled data to be trained. However, since annotating data is time-consuming, it is now necessary to develop systems that show good performance while learning on a limited amount of data. These data must be correctly chosen to obtain models that are still efficient. For this, the systems must be able to determine which data should be annotated to achieve the best results. In this paper, we propose four estimators to estimate the confidence of object detection predictions. The first two are based on Monte…
