Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
PAII Inc.（PAII公司）；Vanderbilt University（范德堡大学）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）；Ping An Insurance (Group) Company of China, Ltd.（中国平安保险（集团）股份有限公司）；University of Florida（佛罗里达大学）
Jinzheng Cai, Adam P. Harrison, Youjing Zheng, Ke Yan, Yuankai Huo, Jing Xiao, Lin Yang, Le Lu
备注：
13 pages, 13 figures, to appear in IEEE Transactions on Medical Imaging
Acquiring large-scale medical image data, necessary for training machine learning algorithms, is frequently intractable, due to prohibitive expert-driven annotation costs. Recent datasets extracted from hospital archives, e.g., DeepLesion, have begun to address this problem. However, these are often incompletely or noisily labeled, e.g., DeepLesion leaves over 50% of its lesions unlabeled. Thus, effective methods to harvest missing annotations are critical for continued progress in medical image analysis. This is the goal of our work, where we develop a powerful system to harvest missing lesions from the DeepLesion dataset at high precision. Accepting the need for some degree of expert labor to achieve high fidelity, we exploit a small fully-labeled subset of medical image volumes and use it to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator and a very selective lesion proposal classifier. While our framework is generic, we optimize our performance by proposing a 3D contextual lesion proposal generator and by using a multi-view multi-scale lesion proposal classifier. These produce harvested and hard-negative proposals, which we then re-use to finetune our proposal generator by using a novel hard negative suppression loss, continuing this process until no extra lesions are found. Extensive experimental analysis demonstrates that our method can harvest an additional 9,805 lesions while keeping precision above 90%. To demonstrate the benefits of our approach, we show that lesion detectors trained on our harvested lesions can significantly outperform the same variants only trained on the original annotations, with boost of average precision of 7% to 10%. We open source our annotations at https://github.com/JimmyCai91/DeepLesionAnnotation.
Semi-supervised learning has the potential to improve the data-efficiency of training data-hungry deep neural networks, which is especially important for medical image analysis tasks where labeled data is scarce. In this work, we present a simple semi-supervised learning method for lesion segmentation tasks based on the ideas of cut-paste augmentation and consistency regularization. By exploiting the mask information available in the labeled data, we synthesize partially labeled samples from the unlabeled images so that the usual supervised learning objective (e.g., binary cross entropy) can b…
Fully annotated large-scale medical image datasets are highly valuable. However, because labeling medical images is tedious and requires specialized knowledge, the large-scale datasets available often have missing annotation issues. For instance, DeepLesion, a large-scale CT image dataset with labels for various kinds of lesions, is reported to have a missing annotation rate of 50\%. Directly training a lesion detector on it would suffer from false negative supervision caused by unannotated lesions. To address this issue, previous works have used sophisticated multi-stage strategies to switch …
Lesion detection is an important problem within medical imaging analysis. Most previous work focuses on detecting and segmenting a specialized category of lesions (e.g., lung nodules). However, in clinical practice, radiologists are responsible for finding all possible types of anomalies. The task of universal lesion detection (ULD) was proposed to address this challenge by detecting a large variety of lesions from the whole body. There are multiple heterogeneously labeled datasets with varying label completeness: DeepLesion, the largest dataset of 32,735 annotated lesions of various types, bu…
PAII Inc.（PAII公司）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）；Ping An Insurance (Group) Company of China, Ltd.（中国平安保险（集团）股份有限公司）
Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as …
SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT
Christian Doppler Laboratory for Artificial Intelligence in Retina（基督教多普勒人工智能视网膜实验室）；Institute of Artificial Intelligence（人工智能研究所）；Center for Medical Data Science（医学数据科学中心）；Medical University of Vienna（维也纳医学大学）；Computational Imaging Research Lab（计算成像研究实验室）；Department of Biomedical Imaging and Image-Guided Therapy（生物医学成像与影像引导治疗部门）；Department of Ophthalmology and Optometry（眼科与视光学部门）
Sun Yat-sen University（中山大学）；Southern University of Science and Technology（南方科技大学）
Manually annotating medical images is extremely expensive, especially for large-scale datasets. Self-supervised contrastive learning has been explored to learn feature representations from unlabeled images. However, unlike natural images, the application of contrastive learning to medical images is relatively limited. In this work, we propose a self-supervised framework, namely lesion-based contrastive learning for automated diabetic retinopathy (DR) grading. Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the fea…
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Inc.（Imsight医疗科技公司）
Automatic skin lesion segmentation on dermoscopic images is an essential component in computer-aided diagnosis of melanoma. Recently, many fully supervised deep learning based methods have been proposed for automatic skin lesion segmentation. However, these approaches require massive pixel-wise annotation from experienced dermatologists, which is very costly and time-consuming. In this paper, we present a novel semi-supervised method for skin lesion segmentation by leveraging both labeled and unlabeled data. The network is optimized by the weighted combination of a common supervised loss for l…
The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. In this paper, we present a novel relation-driven semi-supervised framework for medical image classification. It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to prod…
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）；Imsight AI Research Lab（Imsight人工智能研究实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Chest X-ray (CXR) is the most typical diagnostic X-ray examination for screening various thoracic diseases. Automatically localizing lesions from CXR is promising for alleviating radiologists' reading burden. However, CXR datasets are often with massive image-level annotations and scarce lesion-level annotations, and more often, without annotations. Thus far, unifying different supervision granularities to develop thoracic disease detection algorithms has not been comprehensively addressed. In this paper, we present OXnet, the first deep omni-supervised thoracic disease detection network to ou…
PAII Inc.（PAII公司）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Chang Gung Memorial Hospital（长庚纪念医院）；PingAn Technology（平安科技）
In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of …
Shanghai Jiao Tong University（上海交通大学）；Xi’an Jiaotong University（西安交通大学）；The University of Sydney（悉尼大学）；Shanghai Tenth People’s Hospital, Tongji University（同济大学附属上海市第十人民医院）；Changchun GeneScience Pharmaceutical Co. LTD（长春金赛药业股份有限公司）
Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative f…
Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via perturbations around single data points. In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and…
The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）
Deep convolutional neural networks have achieved remarkable progress on a variety of medical image computing tasks. A common problem when applying supervised deep learning methods to medical images is the lack of labeled data, which is very expensive and time-consuming to be collected. In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. To utilize the unlabeled data, our method encourag…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-superv…
Supervised learning has proved effective for medical image analysis. However, it can utilize only the small labeled portion of data; it fails to leverage the large amounts of unlabeled data that is often available in medical image datasets. Supervised models are further handicapped by domain shifts, when the labeled dataset, despite being large enough, fails to cover different protocols or ethnicities. In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student sem…
The Chinese University of Hong Kong（香港中文大学）；Beihang University（北京航空航天大学）；The University of British Columbia（不列颠哥伦比亚大学）
Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use. In this paper, we study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi), which allows all clients to have only unlabeled data while the server just has a small amount of labeled data. This imFed-Semi problem is addressed by a novel dynamic bank learning scheme, which improves client training by exploiting class proportion information. This scheme consist…
Sun Yat-sen University（中山大学）；The University of Hong Kong（香港大学）；Deepwise AI Lab（深睿人工智能实验室）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Biomedical image segmentation plays a significant role in computer-aided diagnosis. However, existing CNN based methods rely heavily on massive manual annotations, which are very expensive and require huge human resources. In this work, we adopt a coarse-to-fine strategy and propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. Specifically, we design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting, respectively. In the first phase, only the segmentation branch is used…
While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of unlabeled data, and that these representations can improve the performance of supervised tasks. Here, we demonstrate that an unsupervised jigsaw learning task, in combination with supervised training, results in up to a 9.8% improvement in correctly classifying lesions in colono…
Imsight AI Research Lab（Imsight人工智能研究实验室）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide. Automatically segmenting lesions from chest Computed Tomography (CT) is a promising way to assist doctors in COVID-19 screening, treatment planning, and follow-up monitoring. However, voxel-wise annotations are extremely expert-demanding and scarce, especially when it comes to novel diseases, while an abundance of unlabeled data could be available. To tackle the challenge of limited annotations, in this paper, we propose an uncertainty-guided dual-consistency learning netw…
Department of Computer Science University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）；Department of Ophthalmology Central South University（中南大学湘雅医学院（注：原文仅显示Department of Ophthalmology，此处“Central South University”为大学，按要求提取到大学级别））
Diabetic retinopathy (DR) is a primary cause of blindness in working-age people worldwide. About 3 to 4 million people with diabetes become blind because of DR every year. Diagnosis of DR through color fundus images is a common approach to mitigate such problem. However, DR diagnosis is a difficult and time consuming task, which requires experienced clinicians to identify the presence and significance of many small features on high resolution images. Convolutional Neural Network (CNN) has proved to be a promising approach for automatic biomedical image analysis recently. In this work, we inves…
Local discriminative representation is needed in many medical image analysis tasks such as identifying sub-types of lesion or segmenting detailed components of anatomical structures. However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks. In order to avoid the limitations of these two methods, we introduce local discrimination into unsupervised re…
Arizona State University（亚利桑那州立大学）；Mayo Clinic（梅奥诊所）
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can significantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, the first framework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-…
Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extremely time-consuming process. To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN). An encoder-decoder based DCNN is initially trained using a few annotated training samples. This …
Vanderbilt University（范德堡大学）；Central South University（中南大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills in order to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a sin…
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-training based on ImageNet is a common practice to gain better generalization when the data amount is limited. However, it suffers from the domain gap between natural and medical images. In this work, we pre-train DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in medical US applications. To learn US image representations bas…
Deep learning-based Computer-Aided Diagnosis (CAD) has attracted appealing attention in academic researches and clinical applications. Nevertheless, the Convolutional Neural Networks (CNNs) diagnosis system heavily relies on the well-labeled lesion dataset, and the sensitivity to the variation of data distribution also restricts the potential application of CNNs in CAD. Unsupervised Domain Adaptation (UDA) methods are developed to solve the expensive annotation and domain gaps problem and have achieved remarkable success in medical image analysis. Yet existing UDA approaches only adapt knowled…
University of Chinese Academy of Sciences（中国科学院大学）；Huaqiao University（华侨大学）；University of Arkansas at Little Rock（阿肯色大学小石城分校）
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer of medical images, due to the diverse appearances of same lesion among different datasets; 2) equal attention has been paid into all semantic representations instead of neglecting irrelevant knowledge, which leads to negative transfer of untransferable knowledge. To address these challenges, we develop a new unsupervised semantic transfer model including two complementary modules (i.e., T_D and T_F ) for endoscopic l…
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
Alibaba Group DAMO Academy（阿里巴巴集团达摩院）；PAII Inc.（PAII公司）；Ping An Insurance (Group) Company of China, Ltd.（中国平安保险（集团）股份有限公司）；Beijing United Family Hospital（北京和睦家医院）
Massachusetts General Hospital（麻省总医院）；Zhejiang University（浙江大学）；University of Michigan（密歇根大学）；Beijing Institute of Technology（北京理工大学）
Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically,…
National Yang Ming Chiao Tung University（阳明交通大学）；National Cheng Kung University Hospital（国立成功大学医学院附设医院）；National Cheng Kung University（国立成功大学）
This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas in computed tomography pulmonary angiogram (CTPA) images. In current studies, all of the PE CTPA image segmentation methods are trained by supervised learning. However, the supervised learning models need to be retrained and the images need to be relabeled when the CTPA images come from different hospitals. This study proposed a semi-supervised learning method to make the model applicable to different datasets by adding a small amount of un…
Background: Cervical cancer seriously affects the health of the female reproductive system. Optical coherence tomography (OCT) emerged as a non-invasive, high-resolution imaging technology for cervical disease detection. However, OCT image annotation is knowledge-intensive and time-consuming, which impedes the training process of deep-learning-based classification models. Purpose: This study aims to develop a computer-aided diagnosis (CADx) approach to classifying in-vivo cervical OCT images based on self-supervised learning. Methods: In addition to high-level semantic features extracted by a …
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；Lund University（隆德大学）；Cambridge Univ. Hospitals NHS Foundation Trust（剑桥大学医院NHS基金会信托）；Technical University of Munich（慕尼黑工业大学）
Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the un…
Towards Enhanced Analysis of Lung Cancer Lesions in EBUS-TBNA -- A Semi-Supervised Video Object Detection Method
National Yang Ming Chiao Tung University（阳明交通大学）；National Taiwan University Cancer Center（台湾大学癌症中心）；National Taiwan University Hospital（台湾大学医院）；National Taiwan University Hsin-Chu Hospital（台湾大学新竹医院）
FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation
Nankai University（南开大学）；Haihe Lab of ITAI（海河人工智能研究院）；Sichuan University（四川大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）
Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models require large amounts of pixel-level ground truth masks, which can be prohibitive to obtain in the medical domain. Furthermore, training such models in a low-data regime highly increases the risk of overfitting. Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm, which addresses this shortcoming by learning a novel class from only a f…
Tongji University（同济大学）；University of Shanghai for Science and Technology（上海理工大学）；National University of Singapore（新加坡国立大学）
The application of deep learning to medical image segmentation has been hampered due to the lack of abundant pixel-level annotated data. Few-shot Semantic Segmentation (FSS) is a promising strategy for breaking the deadlock. However, a high-performing FSS model still requires sufficient pixel-level annotated classes for training to avoid overfitting, which leads to its performance bottleneck in medical image segmentation due to the unmet need for annotations. Thus, semi-supervised FSS for medical images is accordingly proposed to utilize unlabeled data for further performance improvement. Neve…
Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial infarction) or the brain (stroke). Clinical studies show that plaque composition plays a more important role than lesion size in plaque rupture or erosion events. To determine the plaque composition, various cell types in 3D cardiovascular immunofluorescent images of plaque lesions are counted. However, counting these cells manually is expensive, …
Sun Yat-Sen University（中山大学）；Xidian University（西安电子科技大学）；The Sixth Affiliated Hospital, Sun Yat-sen University（中山大学附属第六医院）
Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large number of unlabeled images for network training, is beneficial for relieving the burden of data annotation in medical image segmentation. According to the experience of medical imaging experts, local attributes such as texture, luster and smoothness are very important factors for identifying target objects like lesions and polyps in medical images. Motivated by this, we propose a cross-level contrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image …
Technical University of Munich（慕尼黑工业大学）；University hospital of Technical University of Munich（慕尼黑工业大学附属医院）；Southern University of Science and Technology（南方科技大学）
In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e.g. a new centreor a new scanner. In this paper, we propose an unsupervised domain adaptation framework for boosting image segmentation performance across multiple domains without using any manual annotations from the new target domains, but by re-calibrating the networks on few images from the target domain. To achieve this, we enforce architectures to be adaptive to new data by re…
Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（健康科技与信息学系，香港理工大学）；Centre for Smart Health and School of Nursing, The Hong Kong Polytechnic University（智能健康中心及护理学院，香港理工大学）；Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong（影像与介入放射科，香港中文大学）
Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education（教育部新一代人工智能技术及其跨学科应用重点实验室）；Nanjing University of Science and Technology（南京理工大学）
