Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries. However, cervical cancer is the leading cause of cancer related deaths in women in India and other low and middle income countries (LMICs) especially among the urban poor and slum dwellers. Several sophisticated techniques such as cytology tests, HPV tests etc. have been widely used for screening of cervical cancer. These tests are inherently time consuming. In this paper, we propose a convolutional autoencoder based framework, having an architecture similar to SegNet which is trained in an adversarial fashion for classifying images of the cervix acquired using a colposcope. We validate performance on the Intel-Mobile ODT cervical image classification dataset. The proposed method outperforms the standard technique of fine-tuning convolutional neural networks pre-trained on ImageNet database with an average accuracy of 73.75%.
Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（Med-X研究院，上海交通大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院脊柱外科部）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院放射科）
Punjab Engineering College (Deemed to be University)（旁遮普工程学院（被视为大学））
Cervical cancer is one of the most common types of cancer found in females. It contributes to 6-29% of all cancers in women. It is caused by the Human Papilloma Virus (HPV). The 5-year survival chances of cervical cancer range from 17%-92% depending upon the stage at which it is detected. Early detection of this disease helps in better treatment and survival rate of the patient. Many deep learning algorithms are being used for the detection of cervical cancer these days. A special category of deep learning techniques known as Generative Adversarial Networks (GANs) are catching up with speed in…
Faculty of Sciences（理学院）；Mohammed V University（穆罕默德五世大学）；École Normale Supérieure（高等师范学院）
Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. A constantly increasing incidence in Africa making cervical cancer a priority by the World Health Organization (WHO) in terms of screening, diagnosis, and treatment. Conventionally, cancer diagnosis relies primarily on histopathological assessment, a deeply error-prone procedure requiring intelligent computer-aided systems as low-cost patient safety mechanisms but lack of labeled data in digital pathology limits their applicability. In this study, few cervical tissue digital slides from…
MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection
Southwest Minzu University（西南民族大学）；The Australian National University（澳大利亚国立大学）；West China Hospital（华西医院）；Sichuan University（四川大学）；La Trobe University（拉筹伯大学）
Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）
Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the same time. Simultaneously, novel machine learning algorithms have boosted the performance of image analysis approaches. In this paper, we focus on a particularly powerful class of architectures, called Generative Adversarial Networks (GANs), applied to histological image data. Besides improving performance, GANs also enable application scenarios…
University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, …
Recent works show that Generative Adversarial Networks (GANs) can be successfully applied to chest X-ray data augmentation for lung disease recognition. However, the implausible and distorted pathology features generated from the less than perfect generator may lead to wrong clinical decisions. Why not keep the original pathology region? We proposed a novel approach that allows our generative model to generate high quality plausible images that contain undistorted pathology areas. The main idea is to design a training scheme based on an image-to-image translation network to introduce variation…
Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance. Typically, the generalization performance of CPath models is analyzed using evaluation protocols such as cross-validation and testing on multi-centric cohorts. However, to ensure that such CPath solutions are robust and safe for use in a clinical setting, a critical analysis of their predictive performance and vulnerability to adversarial attacks is required, which is the focus of this paper. Specifically, we show that a highly accurate model for classi…
The University of Tokyo（东京大学）；National Center for Global Health and Medicine（国立国际医疗研究中心）；Kyushu Medical Center（九州医疗中心）
Convolutional Neural Networks (CNNs) can play a key role in Medical Image Analysis under large-scale annotated datasets. However, preparing such massive dataset is demanding. In this context, Generative Adversarial Networks (GANs) can generate realistic but novel samples, and thus effectively cover the real image distribution. In terms of interpolation, the GAN-based medical image augmentation is reliable because medical modalities can display the human body's strong anatomical consistency at fixed position while clearly reflecting inter-subject variability; thus, we propose to use noise-to-im…
Generative Adversarial Networks for Stain Normalisation in Histopathology
University of Leeds（利兹大学）；Leeds Cancer Centre（利兹癌症中心）；St James’s University Hospital（圣詹姆斯大学医院）；Leeds Institute of Medical Research at St James’s（圣詹姆斯利兹医学研究所）；School of Medicine（医学院）；School of Computing（计算机学院）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心（CISTIB））
University of Strasbourg（斯特拉斯堡大学）；University of Belgrade（贝尔格莱德大学）；University of Kragujevac（克拉古耶瓦茨大学）；Hannover Medical School（汉诺威医学院）；University Clinic, Freiburg（弗赖堡大学诊所）
The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift. Digital Pathology is an area prone to high variability due to many sources, including the common practice of evaluating several consecutive tissue sections stained with different staining protocols. Obtaining labels for each stain is very expensive and time consuming as it requires a high level of domain knowledge. In this article, we propose an unsupervised augmentation approach based on adversarial image-to-image translation, which facilitates the training of stain in…
Cervical cancer is a malignant tumor that seriously threatens women's health, and is one of the most common that affects women worldwide. For its early detection, colposcopic images of the cervix are used for searching for possible injuries or abnormalities. An inherent characteristic of these images is the presence of specular reflections (brightness) that make it difficult to observe some regions, which might imply misdiagnosis. In this paper, a new strategy based on neural networks is introduced for eliminating specular reflections and estimating the unobserved anatomical cervix portion und…
Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation
Imperial College London（伦敦帝国理工学院）；The Babraham Institute（巴布拉汉研究所）；University of Cambridge（剑桥大学）；National Heart & Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
RWTH Aachen University（亚琛工业大学）；University Hospital Düsseldorf（杜塞尔多夫大学医院）；University Hospital Aachen（亚琛大学医院）；ARISTRA GmbH（ARISTRA有限公司）；The Institute for Experimental Molecular Imaging, RWTH Aachen University（亚琛工业大学实验分子影像研究所）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen（亚琛工业大学医院综合诊断中心）
Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts. We let six experienced radiologists rate the interpretability of saliency maps in datasets of X-rays, computed tomography, and magnetic resonance imaging scans. Significant improvements were found for our adversarial models, which could be further improved by the application of dual batch norm…
Institute of Information Systems, HES-SO (University of Applied Sciences and Arts Western Switzerland)（瑞士西部应用科学与艺术大学 信息系统研究所）；Department of Computer Science, University of Geneva（日内瓦大学 计算机科学系）；Department of Neurosciences, University of Padua（帕多瓦大学 神经科学系）；Inselspital, Bern Hospital（伯尔尼大学医院）；Medical Faculty, University of Geneva（日内瓦大学 医学院）
Computational pathology is a domain that aims to develop algorithms to automatically analyze large digitized histopathology images, called whole slide images (WSI). WSIs are produced scanning thin tissue samples that are stained to make specific structures visible. They show stain colour heterogeneity due to different preparation and scanning settings applied across medical centers. Stain colour heterogeneity is a problem to train convolutional neural networks (CNN), the state-of-the-art algorithms for most computational pathology tasks, since CNNs usually underperform when tested on images in…
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University School of Medicine（维克森林大学医学院）；Wake Forest School of Medicine（维克森林大学医学院）；University of Nottingham（诺丁汉大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）；Memorial University of Newfoundland（纽芬兰纪念大学）；University of British Columbia（不列颠哥伦比亚大学）；The University of British Columbia（不列颠哥伦比亚大学）；University College London（伦敦大学学院）
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions. It has been widely used as a auxiliary imaging reference to detect eye-related pathology and predict longitudinal progression of the disease characteristics. Retina layer segmentation is one of the crucial feature extraction techniques, where the variations of retinal layer thicknesses and the retinal layer deformation due to the presence of the fluid are highly correlated with multiple epidemic eye diseases like Diabetic Retinopathy(DR) and Age-related Macular…
University of California, San Francisco（加州大学旧金山分校）
Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single …
Image Data Analytics, Informatics IT, Merck \& Co., Inc., West Point, PA 19486, USA Image Data Analytics, Informatics IT, Merck \& Co., Inc., Rahway, NJ 07065, USA
It is common for pathologists to annotate specific regions of the tissue, such as tumor, directly on the glass slide with markers. Although this practice was helpful prior to the advent of histology whole slide digitization, it often occludes important details which are increasingly relevant to immuno-oncology due to recent advancements in digital pathology imaging techniques. The current work uses a generative adversarial network with cycle loss to remove these annotations while still maintaining the underlying structure of the tissue by solving an image-to-image translation problem. We train…
Beijing University of Posts and Telecommunications（北京邮电大学）；Center for Research on Intelligent Perception and Computing, NLPR, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；Shandong University of Science and Technology（山东科技大学）；Artificial Intelligence Research, CAS (CAS-AIR)（中国科学院人工智能研究院）
In recent years, deep convolutional neural networks have made significant advances in pathology image segmentation. However, pathology image segmentation encounters with a dilemma in which the higher-performance networks generally require more computational resources and storage. This phenomenon limits the employment of high-accuracy networks in real scenes due to the inherent high-resolution of pathological images. To tackle this problem, we propose CoCo DistillNet, a novel Cross-layer Correlation (CoCo) knowledge distillation network for pathological gastric cancer segmentation. Knowledge di…
McGill University（麦吉尔大学）；Centre for Intelligent Machines (CIM)（智能机器中心 (CIM)）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, accurate segmentation requires the inclusion of medical images (e.g., T1 post contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline advers…
CRUK Beatson Institute（CRUK比特森研究所）；Institute of Cancer Sciences（癌症科学研究所）；University of Glasgow（格拉斯哥大学）
Immunohistochemistry is a valuable diagnostic tool for cancer pathology. However, it requires specialist labs and equipment, is time-intensive, and is difficult to reproduce. Consequently, a long term aim is to provide a digital method of recreating physical immunohistochemical stains. Generative Adversarial Networks have become exceedingly advanced at mapping one image type to another and have shown promise at inferring immunostains from haematoxylin and eosin. However, they have a substantial weakness when used with pathology images as they can fabricate structures that are not present in th…
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images
Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；The Hospital for Sick Children（病童医院）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Vector Institute（矢量研究所）；Department of Medical Imaging, University of Toronto（多伦多大学医学影像系）；The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children（病童医院亚瑟与索尼娅·拉巴特脑肿瘤研究中心）；Division of Hematology/Oncology, The Hospital for Sick Children（病童医院血液学/肿瘤学科）；Developmental and Stem Cell Biology Program, The Hospital for Sick Children（病童医院发育与干细胞生物学项目）；Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理系）
Technical University Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能研究所）；Munich School of Data Science (MuDS)（慕尼黑数据科学学院）；ContextVision AB（ContextVision公司）；Institute for Computational Biology, HelmholtzZentrum Munich（慕尼黑亥姆霍兹中心计算生物学研究所）
In digital pathology, different staining procedures and scanners cause substantial color variations in whole-slide images (WSIs), especially across different laboratories. These color shifts result in a poor generalization of deep learning-based methods from the training domain to external pathology data. To increase test performance, stain normalization techniques are used to reduce the variance between training and test domain. Alternatively, color augmentation can be applied during training leading to a more robust model without the extra step of color normalization at test time. We propose…
University of British Columbia（不列颠哥伦比亚大学）；Eindhoven University of Technology（埃因霍温理工大学）；Massachusetts General Hospital（马萨诸塞州综合医院）；Harvard Medical School（哈佛医学院）；BC Cancer Agency（不列颠哥伦比亚癌症机构）
Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality ima…
