This article adresses the problem of automatic squamous cells classification for cervical cancer screening using Deep Learning methods. We study different architectures on a public dataset called Herlev dataset, which consists in classifying cells, obtained by cervical pap smear, regarding the severity of the abnormalities they represent. Furthermore, we use an attribution method to understand which cytomorphological features are actually learned as discriminative to classify severity of the abnormalities. Through this paper, we show how we trained a performant classifier: 74.5\% accuracy on severity classification and 94\% accuracy on normal/abnormal classification.
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）；Asea Brown Boveri Company（阿西亚布朗勃法瑞公司（ABB公司））；Indian Institute of Science Education and Research Kolkata（印度科学教育与研究学院加尔各答分院）
Purpose: Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, as with other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross-attention-based Transfomer approach for the reliable classification of cervical cancer in Pap smear images. Methods: In this study, we propose the CerviFormer -- a model that depends on the Transformers and thereby requires minimal architectural ass…
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…
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）
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 w…
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…
Kyushu University（九州大学）；Kyoto University Hospital（京都大学医院）
Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance …
Automated Cervical Cancer Detection through Visual Inspection with Acetic Acid in Resource-Poor Settings with Lightweight Deep Learning Models Deployed on an Android Device
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Manipal School of Information Sciences, Manipal Academy of Higher Education（曼尼帕尔高等学院信息科学学院）；Department of Obstetrics and Gynecology, Kasturba Medical College（卡斯特尔巴医学院妇产科部门）；NMAM Institute of Technology, Nitte（尼特学院纳米姆技术学院）；Manipal Institute of Technology, Manipal Academy of Higher Education（曼尼帕尔高等学院技术学院）
Centrum Wiskunde & Informatica（荷兰国家数学与计算机科学研究所）；Xomnia B.V.（Xomnia公司）；Erasmus University Medical Center（伊拉斯谟大学医学中心）；Delft University of Technology（代尔夫特理工大学）；Leiden University Medical Center（莱顿大学医学中心）
Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student set…
The Pennsylvania State University（宾夕法尼亚州立大学）；Carnegie Mellon University（卡内基梅隆大学）；Facebook（脸书）
Cervical cancer is the second most prevalent cancer affecting women today. As the early detection of cervical carcinoma relies heavily upon screening and pre-clinical testing, digital cervicography has great potential as a primary or auxiliary screening tool, especially in low-resource regions due to its low cost and easy access. Although an automated cervical dysplasia detection system has been desirable, traditional fully-supervised training of such systems requires large amounts of annotated data which are often labor-intensive to collect. To alleviate the need for much manual annotation, w…
Cervical cancer threatens the health of women seriously. Radiotherapy is one of the main therapy methods but with high risk of acute hematologic toxicity. Delineating the bone marrow (BM) for sparing using computer tomography (CT) images to plan before radiotherapy can effectively avoid this risk. Comparing with magnetic resonance (MR) images, CT lacks the ability to express the activity of BM. Thus, in current clinical practice, medical practitioners manually delineate the BM on CT images by corresponding to MR images. However, the time?consuming delineating BM by hand cannot guarantee the ac…
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯AI实验室）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Peking University Shenzhen Hospital（北京大学深圳医院）；Shenzhen PKU-HKUST Medical Center（深圳北医港科大医疗中心）；Shenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases（深圳市重大妇科疾病早期诊断技术重点实验室）；Harvard University（哈佛大学）
Block-Fused Attention-Driven Adaptively-Pooled ResNet Model for Improved Cervical Cancer Classification
Math of Data Science & Simulation (MODSS) Lab, Computer Science & Engineering（数据科学与模拟（MODSS）实验室，计算机科学与工程）；Indian Institute of Technology Indore（印度理工学院印度尔分校）；Computer Science & Engineering（计算机科学与工程）；Indian Institute of Information Technology Nagpur（印度信息学院纳格浦分校）
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 …
Leiden University Medical Center（莱顿大学医学中心）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究院）
Segmentation of regions of interest in images of patients, is a crucial step in many medical procedures. Deep neural networks have proven to be particularly adept at this task. However, a key question is what type of deep neural network to choose, and whether making a certain choice makes a difference. In this work, we will answer this question for the task of segmentation of the Organs At Risk (OARs) in radiation treatment of cervical cancer (i.e., bladder, bowel, rectum, sigmoid) in Magnetic Resonance Imaging (MRI) scans. We compare several state-of-the-art models belonging to different arch…
Nepal Applied Mathematics and Informatics Institute for research (NAAMII)（尼泊尔应用数学与信息学研究所（NAAMII））；Institute for Implementation Science and Health (IISH)（实施科学与健康研究所（IISH））；Dhulikhel Hospital（杜利克尔医院）；Paropakar Maternity and Women’s Hospital（帕罗帕卡尔妇产医院）；Kathmandu University School of Medical Sciences (KUSMS)（加德满都大学医学院（KUSMS））
Deep learning has achieved unprecedented success in various object detection tasks with huge amounts of labeled data. However, obtaining large-scale annotations for medical images is extremely challenging due to the high demand of labour and expertise. In this paper, we propose a novel deep semi-supervised metric learning method to effectively leverage both labeled and unlabeled data for cervical cancer cell detection. Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels. On the proposal level, we ali…
Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Suzhou Ruiguan Technology Company Ltd.（苏州瑞观科技有限公司）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）；Liaoning Hospital and Institute（辽宁省医院与研究所）
Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated widely to classify cervical pap cells. Most of the existing researches r…
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging
the Institute of Cancer Research（癌症研究所）；The Royal Marsden Hospital（皇家马斯登医院）；Iuliu Hatieganu University of Medicine and Pharmacy（尤利乌·哈蒂耶加努医药大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）
University of Minnesota（明尼苏达大学）；University of Cincinnati（辛辛那提大学）
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis base…
Automated cervical nucleus segmentation based on deep learning can effectively improve the quantitative analysis of cervical cancer. However, accurate nuclei segmentation is still challenging. The classic U-net has not achieved satisfactory results on this task, because it mixes the information of different scales that affect each other, which limits the segmentation accuracy of the model. To solve this problem, we propose a progressive growing U-net (PGU-net+) model, which uses two paradigms to extract image features at different scales in a more independent way. First, we add residual module…
Lister Hill National Center for Biomedical Communications（李斯特希尔国家生物医学通信中心）；National Library of Medicine（美国国家医学图书馆）；National Institutes of Health（美国国立卫生研究院）；Missouri University of Science and Technology（密苏里科技大学）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）；University of Missouri Hospitals and Clinics（密苏里大学医院及诊所）；Stoecker & Associates（斯托克联合事务所）
Cervical cancer is one of the deadliest cancers affecting women globally. Cervical intraepithelial neoplasia (CIN) assessment using histopathological examination of cervical biopsy slides is subject to interobserver variability. Automated processing of digitized histopathology slides has the potential for more accurate classification for CIN grades from normal to increasing grades of pre-malignancy: CIN1, CIN2 and CIN3. Cervix disease is generally understood to progress from the bottom (basement membrane) to the top of the epithelium. To model this relationship of disease severity to spatial d…
Lung cancer has the highest mortality rate of deadly cancers in the world. Early detection is essential to treatment of lung cancer. However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them. Computer-aided diagnosis (CAD) systems have been developed to assist radiologists in nodule detection and diagnosis, greatly easing the workload while increasing diagnosis accuracy. Recent development of deep learning, greatly improved the performance of CAD systems. However, lack of model reliability and interpret…
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Unit of Computer Systems and Bioinformatics, Department of Engineering（计算机系统与生物信息学单位，工程系）；Research Unit of Radiation Oncology, Department of Medicine and Surgery（放射肿瘤研究单位，医学与外科系）；Operative Research Unit of Radiation Oncology（放射肿瘤操作研究单位）；Research Unit of Anatomical Pathology, Department of Medicine and Surgery（解剖病理研究单位，医学与外科系）；Anatomical Pathology Operative Research Unit（解剖病理操作研究单位）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering（诊断与介入部门，放射物理，生物医学工程）
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…
ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer
Flinders University（弗林德斯大学）；The University of Adelaide（阿德莱德大学）；The Australian National University（澳大利亚国立大学）；The University of Sydney（悉尼大学）；Zhejiang University of Technology（浙江工业大学）；Guangdong Technion – Israel Institute of Technology（广东以色列理工学院）；University of Chinese Academy of Sciences（中国科学院大学）；Rice University（莱斯大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；La Trobe University（拉筹伯大学）
Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
Morgan State University（摩根州立大学）；Wrexham University（雷克瑟姆大学）；Federal University of Technology, Minna（明纳联邦理工大学）；Federal University of Education, Kano（卡诺联邦教育大学）
The Pennsylvania State University（宾夕法尼亚州立大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔国家生物医学通信中心）；National Library of Medicine（国家医学图书馆）；Penn State Health Milton S. Hershey Medical Center（宾夕法尼亚州立大学健康米尔顿·S·赫尔希医学中心）；Penn State College Of Medicine（宾夕法尼亚州立大学医学院）
Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realisti…
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, 39762, USA（工业与系统工程系，密苏里州立大学）；Department of Radiation Oncology, University of Mississippi Medical Center, Jackson, MS, 39216, USA（放射肿瘤学系，密苏里大学医学中心）
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
German Cancer Research Center (DKFZ)（德国癌症研究中心）；University Heidelberg（海德堡大学）；RWTH Aachen University（亚琛工业大学）；University of Ulm（乌尔姆大学）；University of Saarland（萨尔大学）；University Hospital Düsseldorf（杜塞尔多夫大学医院）；Medical University of Vienna（维也纳医科大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；Martin Luther University Halle-Wittenberg（马丁路德·哈勒维腾贝格大学）；University Hospital Zurich（苏黎世大学医院）；Städtisches Klinikum Karlsruhe（卡尔斯鲁厄市立医院）；University of Leipzig（莱比锡大学）；University Medical Center Freiburg（弗莱堡大学医学中心）
