Augmenting training datasets has been shown to improve the learning effectiveness for several computer vision tasks. A good augmentation produces an augmented dataset that adds variability while retaining the statistical properties of the original dataset. Some techniques, such as AutoAugment and Fast AutoAugment, have introduced a search phase to find a set of suitable augmentation policies for a given model and dataset. This comes at the cost of great computational overhead, adding up to several thousand GPU hours. More recently RandAugment was proposed to substantially speedup the search phase by approximating the search space by a couple of hyperparameters, but still incurring non-negligible cost for tuning those. In this paper we show that, under the assumption that the augmentation space is approximately distribution invariant, a uniform sampling over the continuous space of augmentation transformations is sufficient to train highly effective models. Based on that result we propose UniformAugment, an automated data augmentation approach that completely avoids a search phase. In addition to discussing the theoretical underpinning supporting our approach, we also use the standard datasets, as well as established models for image classification, to show that UniformAugment's effectiveness is comparable to the aforementioned methods, while still being highly efficient by virtue of not requiring any search.
DIKU University of Copenhagen（哥本哈根大学DIKU）；Biomediq A/S（Biomediq公司）；Cerebriu A/S（Cerebriu公司）；King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) -- a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation in segmen…
Michigan State University（密歇根州立大学）；Mississippi State University（密西西比州立大学）
Weed management plays an important role in many modern agricultural applications. Conventional weed control methods mainly rely on chemical herbicides or hand weeding, which are often cost-ineffective, environmentally unfriendly, or even posing a threat to food safety and human health. Recently, automated/robotic weeding using machine vision systems has seen increased research attention with its potential for precise and individualized weed treatment. However, dedicated, large-scale, and labeled weed image datasets are required to develop robust and effective weed identification systems but th…
Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call DIffuson-based Residual Augmentation Codec (DIRAC), is the first neural codec to allow smooth traversal of the rate-distortion-perception tradeoff at test time, while obtaining competitive performance with GAN-based methods in perceptual quality. Furthermore, while sampling from diffusion probabilistic models is…
Panasonic AI Lab（松下人工智能实验室）；AIoli Labs（AIoli实验室）；Panasonic Technology Division（松下技术部门）
AutoAugment has sparked an interest in automated augmentation methods for deep learning models. These methods estimate image transformation policies for train data that improve generalization to test data. While recent papers evolved in the direction of decreasing policy search complexity, we show that those methods are not robust when applied to biased and noisy data. To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset. In our AutoDO model, we…
Region-of-Interest Augmentation for Mammography Classification under Patient-Level Cross-Validation
Department of Computer Science University of Texas at Arlington（计算机科学系德克萨斯大学阿灵顿分校）；Department of Civil Engineering University of Texas at Arlington（土木工程系德克萨斯大学阿灵顿分校）；Department of Computer Science Colorado State University（计算机科学系科罗拉多州立大学）
Technical University of Denmark（丹麦技术大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original training data; e.g.~new images are formed by rotating old ones. Current augmentation schemes, however, rely on manual specification of the applied transformations, making data augmentation an implicit form of feature engineering. With an eye towards true end-to-end learning, we suggest learning the applied transformations on a per-class basis. Particularly, we align image pairs within each class under the assumption…
Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data. However, the data distribution among clients is naturally non-i.i.d., which leads to severe degradation of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm to address feature shift in clients' samples, which can be caused by various factors, e.g., acquisition differences in medical imaging. To reach this goal, we propose FedFA to tackle federated learning from a distinct perspective of federated featu…
Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting. However, labeled training data for real-world applications such as healthcare is limited and difficult to access given longstanding privacy, and strict data sharing policies. By manipulating image datasets in the pixel or feature space, existing data augmentation techniques represent one of the effective ways to improve the quantity and diversity of training data. Here, we look to advance augmentation techniques by building upon the emerging success of text-to-…
National University of Sciences and Technology (NUST)（国立科技大学）
Table Structure Recognition is an essential part of end-to-end tabular data extraction in document images. The recent success of deep learning model architectures in computer vision remains to be non-reflective in table structure recognition, largely because extensive datasets for this domain are still unavailable while labeling new data is expensive and time-consuming. Traditionally, in computer vision, these challenges are addressed by standard augmentation techniques that are based on image transformations like color jittering and random cropping. As demonstrated by our experiments, these t…
Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning
Computational Health Center & Helmholtz AI, Helmholtz Munich（计算健康中心与亥姆霍兹AI，亥姆霍兹慕尼黑）；Computer Aided Medical Procedures, Technical University of Munich（计算机辅助医疗程序，慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Medicine III, Ludwig Maximilian University Hospital（路德维希·马克西米利安大学医院第三内科）；Department of Physics, Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学物理系）；DKTK, German Cancer Consortium, partner site Munich（德国癌症联盟DKTK，慕尼黑合作站点）
Functional Localization Enforced Deep Anomaly Detection Using Fundus Images
Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希·海涅大学）；Philipps University of Marburg（马尔堡菲利普斯大学）；University Hospital Knappschaftskrankenhaus Bochum（波鸿克纳普schaft医院大学医院）；University of Münster（明斯特大学）
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling
Institut Denis Poisson, University of Orléans（德尼·泊松研究所，奥尔良大学）；University of Toulouse, IRIT/INP-ENSEEIHT, CNRS（图卢兹大学，法国国家信息与自动化研究所/国立高等电子、电子技术、计算机与机械学院，法国国家科学研究中心）
本文提出一种基于去噪正则化（RED）的图像反演贝叶斯框架，并设计了内含Langevin Monte Carlo步骤的分裂Gibbs采样算法，通过去模糊等任务验证了其有效性。
Baidu Research（百度研究院）；University of Technology Sydney（悉尼科技大学）
Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations…
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Graduate School of Engineering, Tohoku University（东北大学工学研究生院）；School of Biomedical Engineering, Anhui Medical University（安徽医科大学生物医学工程学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；The Hong Kong Polytechnic University（香港理工大学）
LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas
University of Lübeck（吕贝克大学）；Institute of Medical Informatics（医学信息学研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；German Aerospace Center（德国航空航天中心）
Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models
Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM)（巴塞罗那人工智能在医学实验室（BCN-AIM））；Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain（数学与计算机科学系，巴塞罗那大学，西班牙）；Helmholtz Center Munich（海德堡研究中心慕尼黑）；Technical University of Munich（慕尼黑技术大学）；William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, UK（威廉·哈维研究所，英国国家健康研究院巴特研究所，伦敦女王玛丽大学，查尔斯广场，伦敦，英国）；Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK（巴特心脏中心，圣巴塞洛缪医院，巴特健康国家卫生信托，西史密斯菲尔德，伦敦，英国）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与教育研究所（ICREA））
Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models
Tecnologico de Monterrey, School of Engineering and Sciences（蒙特雷科技大学工程与科学学院）；School of Engineering, Pontificia Universidad Católica de Chile（智利天主教大学工程学院）；Tecnologico de Monterrey, School of Medicine and Health Sciences（蒙特雷科技大学医学与健康科学学院）
