With the increase of computing power, machine learning models in medical imaging have been introduced to help in rending medical diagnosis and inspection, like hemophilia, a rare disorder in which blood cannot clot normally. Often, one of the bottlenecks of detecting hemophilia is the lack of data available to train the algorithm to increase the accuracy. As a possible solution, this research investigated whether introducing augmented data by data synthesis or traditional augmentation techniques can improve model accuracy, helping to diagnose the diseases. To tackle this research, features of ultrasound images were extracted by the pre-trained VGG-16, and similarities were compared by cosine similarity measure based on extracted features in different distributions among real images, synthetic images, and augmentation images (Real vs. Real, Syn vs. Syn, Real vs. Different Batches of Syn, Real vs. Augmentation Techniques). Model testing performance was investigated using EffientNet-B4 to recognize "blood" images with two augmentation methods. In addition, a gradient-weighted class activation mapping (Grad-CAM) visualization was used to interpret the unexpected results like loss of accuracy. Synthetic and real images do not show high similarity, with a mean similarity score of 0.4737. Synthetic batch 1 dataset and images by horizontal flip are more similar to the original images. Classic augmentation techniques and data synthesis can improve model accuracy, and data by traditional augmentation techniques have a better performance than synthetic data. In addition, the Grad-CAM heatmap figured out the loss of accuracy is due to a shift in the domain. Overall, this research found that two augmentation methods, data synthesis and traditional augmentation techniques, both can improve accuracy to a certain extent to help to diagnose rare diseases.
An important component of human analysis of medical images and their context is the ability to relate newly seen things to related instances in our memory. In this paper we mimic this ability by using multi-modal retrieval augmentation and apply it to several tasks in chest X-ray analysis. By retrieving similar images and/or radiology reports we expand and regularize the case at hand with additional knowledge, while maintaining factual knowledge consistency. The method consists of two components. First, vision and language modalities are aligned using a pre-trained CLIP model. To enforce that …
Technische Hochschule Ulm - Ulm University of Applied Sciences（乌尔姆应用技术大学）；Universitätsklinikum Ulm - Ulm University Medical Center（乌尔姆大学医学中心）
Since the beginning of the COVID-19 pandemic, researchers have developed deep learning models to classify COVID-19 induced pneumonia. As with many medical imaging tasks, the quality and quantity of the available data is often limited. In this work we train a deep learning model on publicly available COVID-19 image data and evaluate the model on local hospital chest X-ray data. The data has been reviewed and labeled by two radiologists to ensure a high quality estimation of the generalization capabilities of the model. Furthermore, we are using a Generative Adversarial Network to generate synth…
ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation
The Hong Kong Polytechnic University（香港理工大学）；Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation
AGH University of Krakow（克拉科夫AGH科技大学）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学大学（HES-SO））；Radboud University Medical Center（拉德堡德大学医学中心）；University of Padova（帕多瓦大学）；University of Geneva（日内瓦大学）
Gazi University（加齐大学）；Basarsoft Information Systems Inc.（巴沙尔软件信息系统公司）
Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints. FL mainly or partially supports data privacy and security issues and provides an alternative to model problems facilitating multiple edge devices or organizations to contribute a training of a global model using a number of local data without having them. Non-IID data of FL caused from its distributed nature presents a significant performance degradation and stabilization skews. This paper introduces…
Tel-Aviv University（特拉维夫大学）；The Interdisciplinary Center Herzliya（赫兹利亚跨学科研究中心）
Image augmentation techniques apply transformation functions such as rotation, shearing, or color distortion on an input image. These augmentations were proven useful in improving neural networks' generalization ability. In this paper, we present a novel augmentation operation, InAugment, that exploits image internal statistics. The key idea is to copy patches from the image itself, apply augmentation operations on them, and paste them back at random positions on the same image. This method is simple and easy to implement and can be incorporated with existing augmentation techniques. We test I…
Although Convolutional Neural Networks (CNNs) have high accuracy in image recognition, they are vulnerable to adversarial examples and out-of-distribution data, and the difference from human recognition has been pointed out. In order to improve the robustness against out-of-distribution data, we present a frequency-based data augmentation technique that replaces the frequency components with other images of the same class. When the training data are CIFAR10 and the out-of-distribution data are SVHN, the Area Under Receiver Operating Characteristic (AUROC) curve of the model trained with the pr…
Handwritten mathematical expression recognition (HMER) is an important research direction in handwriting recognition. The performance of HMER suffers from the two-dimensional structure of mathematical expressions (MEs). To address this issue, in this paper, we propose a high-performance HMER model with scale augmentation and drop attention. Specifically, tackling ME with unstable scale in both horizontal and vertical directions, scale augmentation improves the performance of the model on MEs of various scales. An attention-based encoder-decoder network is used for extracting features and gener…
Data augmentation has been widely adopted for object detection in 3D point clouds. However, all previous related efforts have focused on manually designing specific data augmentation methods for individual architectures. In this work, we present the first attempt to automate the design of data augmentation policies for 3D object detection. We introduce the Progressive Population Based Augmentation (PPBA) algorithm, which learns to optimize augmentation strategies by narrowing down the search space and adopting the best parameters discovered in previous iterations. On the KITTI 3D detection tes…
Data augmentation reduces the generalization error by forcing a model to learn invariant representations given different transformations of the input image. In computer vision, on top of the standard image processing functions, data augmentation techniques based on regional dropout such as CutOut, MixUp, and CutMix and policy-based selection such as AutoAugment demonstrated state-of-the-art (SOTA) results. With an increasing number of data augmentation algorithms being proposed, the focus is always on optimizing the input-output mapping while not realizing that there might be an untapped value…
Jadavpur University（贾达布尔大学）；Nanyang Technological University（南洋理工大学）；Cognizant Technology Solutions（高知特科技解决方案公司）；Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
Binarization of degraded document images is an elementary step in most of the problems in document image analysis domain. The paper re-visits the binarization problem by introducing an adversarial learning approach. We construct a Texture Augmentation Network that transfers the texture element of a degraded reference document image to a clean binary image. In this way, the network creates multiple versions of the same textual content with various noisy textures, thus enlarging the available document binarization datasets. At last, the newly generated images are passed through a Binarization ne…
Improving the Transferability of Adversarial Examples by Feature Augmentation
Zhejiang University（浙江大学）；Defense Innovation Institute, Chinese Academy of Military Science（中国军事科学院国防创新研究院）；Intelligent Game and Decision Laboratory（智能博弈与决策实验室）
MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness
National Institute of Advanced Industrial Science and Technology (AIST)（国立研究开发法人产业技术综合研究所）；Tokyo Denki University（东京电机大学）；Waseda University（早稻田大学）；Fundamental AI Lab, University of Technology Nuremberg（纽伦堡工业大学基础人工智能实验室）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
Multi-task learning (MTL) has received considerable attention, and numerous deep learning applications benefit from MTL with multiple objectives. However, constructing multiple related tasks is difficult, and sometimes only a single task is available for training in a dataset. To tackle this problem, we explored the idea of using unsupervised clustering to construct a variety of auxiliary tasks from unlabeled data or existing labeled data. We found that some of these newly constructed tasks could exhibit semantic meanings corresponding to certain human-specific attributes, but some were non-id…
In past research on self-supervised learning for image classification, the use of rotation as an augmentation has been common. However, relying solely on rotation as a self-supervised transformation can limit the ability of the model to learn rich features from the data. In this paper, we propose a novel approach to self-supervised learning for image classification using several localizable augmentations with the combination of the gating method. Our approach uses flip and shuffle channel augmentations in addition to the rotation, allowing the model to learn rich features from the data. Furthe…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Department of Radiology, University Medical Center Utrecht（乌得勒支大学医学中心放射科）
Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning methods for segmentation in CCTA might suffer from differences in contrast-agent attenuation between training and test data due to non-standardized contrast administration protocols and varying cardiac output. We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent. We compare this to an augmentation by…
Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；A.I. Virtanen Institute, University of Eastern Finland（东芬兰大学人工智能维尔塔宁研究所）；Aarhus University（奥胡斯大学）
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
Shanghai Jiao Tong University（上海交通大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)（中国科学院智能信息处理重点实验室）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Pengcheng Laboratory（鹏城实验室）；Alibaba Group（阿里巴巴集团）；Tsinghua University（清华大学）
The Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）；Huawei Singular Security Lab（华为奇安安全实验室）
Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks craft adversarial samples with a local model and have attracted increasing attention from researchers due to their high efficiency. The state-of-the-art transfer-based attacks are generally based on data augmentation, which typically augments …
University of California San Diego（加利福尼亚大学圣迭戈分校）；CUNY Graduate Center（纽约市立大学研究生中心）；Facebook AI Research（Facebook人工智能研究院）
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, ignoring the semantic relevance of parts of an image-e.g. a subject vs. a background-which can lead to the learning of spurious correlations. Our work addresses this problem by investigating a class of simple, yet highly eff…
SenseTime Research（商汤科技研究院）；Beihang University（北京航空航天大学）；University of Sydney（悉尼大学）
The recent progress on automatically searching augmentation policies has boosted the performance substantially for various tasks. A key component of automatic augmentation search is the evaluation process for a particular augmentation policy, which is utilized to return reward and usually runs thousands of times. A plain evaluation process, which includes full model training and validation, would be time-consuming. To achieve efficiency, many choose to sacrifice evaluation reliability for speed. In this paper, we dive into the dynamics of augmented training of the model. This inspires us to de…
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Shanghai Shizhuang Information Technology Co., Ltd（上海石庄信息技术有限公司）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
本文提出 DPA 对抗攻击方法，通过 Diverse Parameters Optimization 和 Hard Model Aggregation 两个阶段引入多样化参数初始化与有益扰动以增强替代模型多样性，有效提升了人脸识别对抗样本的迁移性。
Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robustness and accuracy, which is exemplified by standard data augment techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a si…
As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real world have received increasing attention. Data augmentation is a widely used method to improve model performance, and some recent works have also confirmed its positive effect on the robustness of AI models. However, most of the existing data augmentation methods are heuristic, lacking the exploration of their internal mechanisms. We apply the explainable artificial intelligence (XAI) method, explore the internal me…
TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision
Nepal Applied Mathematics and Informatics Institute for research(NAAMII)（尼泊尔应用数学与信息学研究所（NAAMII））；Fogsphere(Redev AI Ltd.)（Fogsphere（Redev AI有限公司））；School of Natural and Computing Sciences, University of Aberdeen（阿伯丁大学自然与计算科学学院）；West Virginia University（西弗吉尼亚大学）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Maastricht University（马斯特里赫特大学）
Sequence-to-Sequence (S2S) models recently started to show state-of-the-art performance for automatic speech recognition (ASR). With these large and deep models overfitting remains the largest problem, outweighing performance improvements that can be obtained from better architectures. One solution to the overfitting problem is increasing the amount of available training data and the variety exhibited by the training data with the help of data augmentation. In this paper we examine the influence of three data augmentation methods on the performance of two S2S model architectures. One of the da…
We investigate the robustness of vision transformers (ViTs) through the lens of their special patch-based architectural structure, i.e., they process an image as a sequence of image patches. We find that ViTs are surprisingly insensitive to patch-based transformations, even when the transformation largely destroys the original semantics and makes the image unrecognizable by humans. This indicates that ViTs heavily use features that survived such transformations but are generally not indicative of the semantic class to humans. Further investigations show that these features are useful but non-r…
University Politehnica of Bucharest（布加勒斯特理工大学）；University of Bucharest（布加勒斯特大学）
The task of detecting whether a person wears a face mask from speech is useful in modelling speech in forensic investigations, communication between surgeons or people protecting themselves against infectious diseases such as COVID-19. In this paper, we propose a novel data augmentation approach for mask detection from speech. Our approach is based on (i) training Generative Adversarial Networks (GANs) with cycle-consistency loss to translate unpaired utterances between two classes (with mask and without mask), and on (ii) generating new training utterances using the cycle-consistent GANs, ass…
Bosch Center for Artificial Intelligence（博世人工智能中心）
Despite data augmentation being a de facto technique for boosting the performance of deep neural networks, little attention has been paid to developing augmentation strategies for generative adversarial networks (GANs). To this end, we introduce a novel augmentation scheme designed specifically for GAN-based semantic image synthesis models. We propose to randomly warp object shapes in the semantic label maps used as an input to the generator. The local shape discrepancies between the warped and non-warped label maps and images enable the GAN to learn better the structural and geometric details…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；University of Michigan（密歇根大学）；University of California, Berkeley（加州大学伯克利分校）；Amazon Web Services（亚马逊云科技）；LG AI Research（LG人工智能研究院）
Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random cropping and color jittering. However, such invariance could be harmful to downstream tasks if they rely on the characteristics of the data augmentations, e.g., location- or color-sensitive. This is not an issue just for unsupervised learning; we found that this occurs even in supervised learning because it also learns to predict the same label for all augmented samples of an instance. To avoid such failures and obt…
Charles River Analytics（查尔斯河分析公司）；U.S. Army CCDC C5ISR Center（美国陆军作战能力发展司令部C5ISR中心）
Recent advances in machine learning (ML) and computer vision tools have enabled applications in a wide variety of arenas such as financial analytics, medical diagnostics, and even within the Department of Defense. However, their widespread implementation in real-world use cases poses several challenges: (1) many applications are highly specialized, and hence operate in a \emph{sparse data} domain; (2) ML tools are sensitive to their training sets and typically require cumbersome, labor-intensive data collection and data labelling processes; and (3) ML tools can be extremely "black box," offeri…
Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image synthesis has not been thoroughly investigated in previous studies. In this work, we systematically study the effectiveness of various existing augmentation techniques for GAN training in a variety of settings. We provide insights and guidelines on how to augment images for both vanilla GANs and GANs with regularizations, improving the fidelity of the generated images substantially. Surprisingly, we find that vanilla G…
SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）
Shahjalal University of Science and Technology（沙贾拉尔科技大学）；Mapbox；University of Alberta（阿尔伯塔大学）；Fordham University（福特汉姆大学）
The creation of altered and manipulated faces has become more common due to the improvement of DeepFake generation methods. Simultaneously, we have seen detection models' development for differentiating between a manipulated and original face from image or video content. In this paper, we focus on identifying the limitations and shortcomings of existing deepfake detection frameworks. We identified some key problems surrounding deepfake detection through quantitative and qualitative analysis of existing methods and datasets. We found that deepfake datasets are highly oversampled, causing models…
