DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
Georgia Institute of Technology（佐治亚理工学院）；University of Cambridge（剑桥大学）；Bern University Hospital（伯尔尼大学医院）；National Taiwan University（台湾大学）；University of California, Berkeley（加州大学伯克利分校）；Tri-Service General Hospital（三军总医院）；Beijing Friendship Hospital（北京友谊医院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Amsterdam（阿姆斯特丹大学）
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show, the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.
University of Amsterdam（阿姆斯特丹大学）；Georgia Institute of Technology（佐治亚理工学院）
Medical image captioning automatically generates a medical description to describe the content of a given medical image. A traditional medical image captioning model creates a medical description only based on a single medical image input. Hence, an abstract medical description or concept is hard to be generated based on the traditional approach. Such a method limits the effectiveness of medical image captioning. Multi-modal medical image captioning is one of the approaches utilized to address this problem. In multi-modal medical image captioning, textual input, e.g., expert-defined keywords, …
Medical Image Registration and Its Application in Retinal Images: A Review
Southern University of Science and Technology（南方科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Singapore Eye Research Institute（新加坡眼科研究所）
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for th…
We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images. In quantitative measurements and a qualitative evaluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model's feature maps reveals that it has learned to distinguish retinal l…
Retinal Optical Coherence Tomography (OCT), a noninvasive cross-sectional scan of the eye with qualitative 3D visualization of the retinal anatomy is use to study the retinal structure and the presence of pathogens. The advent of the retinal OCT has transformed ophthalmology and it is currently paramount for the diagnosis, monitoring and treatment of many eye pathogens including Macular Edema which impairs vision severely or Glaucoma that can cause irreversible blindness. However the quality of retinal OCT images varies among device manufacturers. Deep Learning methods have had their success i…
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Disease Detection from Retinal Fundus Images
Hertie Institute for AI in Brain Health, University of Tübingen, Germany（人工智能与脑健康赫尔特研究所，图宾根大学，德国）；Tübingen AI Center, University of Tübingen, Germany（图宾根人工智能中心，图宾根大学，德国）
Beihang University（北京航空航天大学）；National Institute of Informatics（国立情报学研究所）；Peng Cheng Laboratory（鹏城实验室）；Central South University（中南大学）
Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based meth…
A BERT-Style Self-Supervised Learning CNN for Disease Identification from Retinal Images
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；McKeley School of Engineering, Washington University in St. Louis（工程学院，华盛顿大学圣路易斯分校）；Department of Neurology, Mayo Clinic（神经病学部，梅奥诊所）
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Retinal vessel information is helpful in retinal disease screening and diagnosis. Retinal vessel segmentation provides useful information about vessels and can be used by physicians during intraocular surgery and retinal diagnostic operations. Convolutional neural networks (CNNs) are powerful tools for classification and segmentation of medical images. Complexity of CNNs makes it difficult to implement them in portable devices such as binocular indirect ophthalmoscopes. In this paper a simplification approach is proposed for CNNs based on combination of quantization and pruning. Fully connecte…
Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present …
JIS College of Engineering（JIS工程学院）；Kalyani Govt. Engg. College（卡利亚尼政府工程学院）
Digital Retinal Fundus Images helps to detect various ophthalmic diseases by detecting morphological changes in optical cup, optical disc and macula. Present work proposes a method for the authentication of medical images based on Discrete Wavelet Transformation (DWT) and Spread Spectrum. Proper selection of the Non Region of Interest (NROI) for watermarking is crucial, as the area under concern has to be the least required portion conveying any medical information. Proposed method discusses both the selection of least impact area and the blind watermarking technique. Watermark is embedded wit…
BRAC University（BRAC大学）；Islamic University of Technology（伊斯兰技术大学）
In the world of medical diagnostics, the adoption of various deep learning techniques is quite common as well as effective, and its statement is equally true when it comes to implementing it into the retina Optical Coherence Tomography (OCT) sector, but (i)These techniques have the black box characteristics that prevent the medical professionals to completely trust the results generated from them (ii)Lack of precision of these methods restricts their implementation in clinical and complex cases (iii)The existing works and models on the OCT classification are substantially large and complicated…
Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the retinal vasculature, to train an AI algorithm to generate vasculature maps from standard structural optical coherence tomography (OCT) images of the same retinae, both exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer perfusion of …
According to the World Health Organization, 285 million people worldwide live with visual impairment. The most commonly used imaging technique for diagnosis in ophthalmology is optical coherence tomography (OCT). However, analysis of retinal OCT requires trained ophthalmologists and time, making a comprehensive early diagnosis unlikely. A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images. The performance of the tool in classifying retinal conditions was on par to that of trained medical experts. …
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；University of California, Berkeley（加州大学伯克利分校）；Affiliated Eye Hospital of Nanchang University（南昌大学附属眼科医院）
Retinal vascular diseases affect the well-being of human body and sometimes provide vital signs of otherwise undetected bodily damage. Recently, deep learning techniques have been successfully applied for detection of diabetic retinopathy (DR). The main obstacle of applying deep learning techniques to detect most other retinal vascular diseases is the limited amount of data available. In this paper, we propose a transfer learning technique that aims to utilize the feature similarities for detecting retinal vascular diseases. We choose the well-studied DR detection as a source task and identify…
Robert Bosch GmbH（罗伯特·博世有限公司）；TU Dortmund University（多特蒙德工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly sho…
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize that the anatomic structure of layers and their high-frequency variation in OCT images make retinal OCT a fitting choice for extracting spectral-domain features and combining them with spatial domain features. In this work, we present $Υ$-Net, an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images. The results of this work demonstrate that…
Metadata-enhanced contrastive learning from retinal optical coherence tomography images
Imperial College London（帝国理工学院）；Medical University of Vienna（维也纳医科大学）；University of Michigan（密歇根大学）；King’s College London（伦敦国王学院）；Institute of Molecular and Clinical Ophthalmology Basel（巴塞尔分子与临床眼科研究所）；Universitat Basel（巴塞尔大学）；University College London（伦敦大学学院）；Moorfields Eye Hospital（摩尔菲尔兹眼科医院）；University of Southampton（南安普顿大学）
Existing supervised approaches didn't make use of the low-level features which are actually effective to this task. And another deficiency is that they didn't consider the relation between pixels, which means effective features are not extracted. In this paper, we proposed a novel convolutional neural network which make sufficient use of low-level features together with high-level features and involves atrous convolution to get multi-scale features which should be considered as effective features. Our model is tested on three standard benchmarks - DRIVE, STARE, and CHASE databases. The results…
Dept. Ophthalmology, Seoul Nat'l University Bundang Hospital , Dept. Electronic Eng., Soonchunhyang Univ.
Retinal images have the highest resolution and clarity among medical images. Thus, vessel analysis in retinal images may facilitate early diagnosis and treatment of many chronic diseases. In this paper, we propose a novel multi-scale residual convolutional neural network structure based on a \emph{scale-space approximation (SSA)} block of layers, comprising subsampling and subsequent upsampling, for multi-scale representation. Through analysis in the frequency domain, we show that this block structure is a close approximation of Gaussian filtering, the operation to achieve scale variations in …
IBM Research - Australia（IBM澳大利亚研究院）；NYU Langone Eye Center（纽约大学兰贡眼科中心）；NYU School of Medicine（纽约大学医学院）
Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the lack of labeled data which is time-consuming and costly to obtain. In this paper, we propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network using limited labeled samples and a large number of unlabeled images. First, a teacher segmentation model is trained from the labeled samples using Bayesian deep learning. The trained model is used to g…
Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis. In this paper, a denoising approach that preserves disease characteristics on retinal optical coherence tomography images in ophthalmology is presented. By combining a deep convolutional autoencoder with a priorly trained ResNet image classifier as regularizer, the perceptibility of delicate details is encouraged and only information-less background noise is filtered out. With our approach, higher peak signal-to-noise ratios with $ \mathrm{PSNR} = 31.2\,\mathrm{dB} $ and hi…
Institute for Infocomm Research, A*STAR, Singapore（信息通信研究所（A*STAR，新加坡））
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require resource-intensive expert annotation. Semi-supervised generative adversarial network (GAN) approaches offer a means to learn from limited labeled data alongside larger unlabeled datasets, but have not been applied to discern fine-scale, sparse or localized features that define medical abnormalities. To overcome these limitations, we propose a patch-based semi-supervised learning approach and evaluate performance on c…
Inception Institute of Artificial Intelligence（人工智能起源研究所）
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. In this paper, we first analyze the ophthalmoscope imaging system and simulate a reliable degradation of major inf…
Patch-Based and Non-Patch-Based inputs Comparison into Deep Neural Models: Application for the Segmentation of Retinal Diseases on Optical Coherence Tomography Volumes
Laboratoire LIMOS, CNRS UMR 6158（利摩日大学实验室，法国国家科研中心UMR 6158联合研究单位）；Université Clermont-Auvergne（克莱蒙奥弗涅大学）；AL Mansour University College（曼苏尔大学学院）
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis
Hong Kong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Khulna University of Engineering and Technology（库尔纳工程技术大学）
In medical science, the use of computer science in disease detection and diagnosis is gaining popularity. Previously, the detection of disease used to take a significant amount of time and was less reliable. Machine learning (ML) techniques employed in recent biomedical researches are making revolutionary changes by gaining higher accuracy with more concise timing. At present, it is even possible to automatically detect diseases from the scanned images with the help of ML. In this research, we have taken such an attempt to detect retinal diseases from optical coherence tomography (OCT) X-ray i…
In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions. In this work, we propose a novel convolutional neural network architecture that can effectively learn to produce a lower-dimensional segmentation mask than the input image. The network restores encoded representation only in a subset of input spatial dimensions and keeps the representation unchanged in the others. The newly proposed projective skip-connections allow linking the encoder and decoder in a UNet-like structure. We evaluated the proposed m…
Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学））；ITER, Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学）ITER学院）；Center for the Internet of Things, ITER, Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学）ITER学院物联网中心）
Extracting blood vessels from retinal fundus images plays a decisive role in diagnosing the progression in pertinent diseases. In medical image analysis, vessel extraction is a semantic binary segmentation problem, where blood vasculature needs to be extracted from the background. Here, we present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. Despite a relatively less number of trainable network parameters, the scaled version of U-net architecture provides better performance compare to other methods in the domain. We validated…
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Stanford University School of Medicine（斯坦福大学医学院）
Beijing University of Posts and Telecommunications（北京邮电大学）
Accurate retinal vessel segmentation is a challenging problem in color fundus image analysis. An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. Technically, this problem suffers from various degrees of vessel thickness, perception of details, and contextual feature fusion. For addressing these challenges, a deep learning based method has been proposed and several customized modules have been integrated into the well-known encoder-decoder architecture U-net, which is mainly employed in medical image segmentation. Structu…
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；University of California, Berkeley（加州大学伯克利分校）；Affiliated Eye Hospital of Nanchang University（南昌大学附属眼科医院）
Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight. ROP is one of the leading causes for infant blindness globally. Early detection of ROP is critical to slow down and avert the progression to vision impairment caused by ROP. Yet there is limited awareness of ROP even among medical professionals. Consequently, dataset for ROP is limited if ever available, and is in general extremely imbalanced in terms of the ratio between negative images and positive ones. In this study, we formulate the proble…
When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images
Technical University of Munich（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学医院）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Sapienza University of Rome（罗马第一大学）；Faculty of Medicine, University of Southampton（南安普顿大学医学院）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS信托基金）；Imperial College London（伦敦帝国学院）
BenchReAD: A systematic benchmark for retinal anomaly detection
The Center for Smart Health, School of Nursing, the Hong Kong Polytechnic University, Hong Kong, China（香港理工大学护理学院智能健康中心）；School of Biomedical Engineering, Tsinghua University, Beijing, China（清华大学生物医学工程学院）；Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（中国科学院深圳先进技术研究院医学人工智能研究中心）
The Hong Kong University of Science and Technology（香港科技大学）
Deep learning techniques have shown their success in medical image segmentation since they are easy to manipulate and robust to various types of datasets. The commonly used loss functions in the deep segmentation task are pixel-wise loss functions. This results in a bottleneck for these models to achieve high precision for complicated structures in biomedical images. For example, the predicted small blood vessels in retinal images are often disconnected or even missed under the supervision of the pixel-wise losses. This paper addresses this problem by introducing a long-range elastic interacti…
We explore the value of weak labels in learning transferable representations for medical images. Compared to hand-labeled datasets, weak or inexact labels can be acquired in large quantities at significantly lower cost and can provide useful training signals for data-hungry models such as deep neural networks. We consider weak labels in the form of pseudo-labels and propose a semi-weakly supervised contrastive learning (SWCL) framework for representation learning using semi-weakly annotated images. Specifically, we train a semi-supervised model to propagate labels from a small dataset consisti…
DGIST（大邱庆北科学技术院）；Yeungnam University College of Medicine（岭南大学医学院）；Yeungnam University Hospital（岭南大学医院）
Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often require a large-scale training dataset and long training times. Therefore, in real world application where only dozens to hundreds of image pairs are available, existing models cannot be practically used. To address these limitations, we propose a novel unsupervised registration model which is integrated with a gradient-based meta learning framework. In particular, we train a meta learner which finds an initialization poin…
