Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer
University of Sydney（悉尼大学）；Royal Prince Alfred Hospital（阿尔弗雷德王子医院）
Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim
备注：
Source code is available from https://github.com/ashnilkumar/colearn . The paper has been accepted for publication in IEEE Transactions on Medical Imaging. The final published version of the manuscript can be accessed from the IEEE. The paper contains 21 pages (14 main paper, 7 supplementary), 16 images (8 main paper, 8 supplementary), and 3 tables
The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT. Current methods for PET-CT image analysis either process the modalities separately or fuse information from each modality based on knowledge about the image analysis task. These methods generally do not consider the spatially varying visual characteristics that encode different information across the different modalities, which have different priorities at different locations. For example, a high abnormal PET uptake in the lungs is more meaningful for tumor detection than physiological PET uptake in the heart. Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis. Our CNN first encodes modality-specific features and then uses them to derive a spatially varying fusion map that quantifies the relative importance of each modality's features across different spatial locations. These fusion maps are then multiplied with the modality-specific feature maps to obtain a representation of the complementary multi-modality information at different locations, which can then be used for image analysis. We evaluated the ability of our CNN to detect and segment multiple regions with different fusion requirements using a dataset of PET-CT images of lung cancer. We compared our method to baseline techniques for multi-modality image fusion and segmentation. Our findings show that our CNN had a significantly higher foreground detection accuracy (99.29%, p < 0.05) than the fusion baselines and a significantly higher Dice score (63.85%) than recent PET-CT tumor segmentation methods.
Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data. In this study, we realize these custom radiomic sequencers as deep convolutional sequencers using a deep convolutional neural network learning architectu…
Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is one of the non-communicable diseases (NCDs) and NCDs accounts for 71% of total deaths worldwide whereas lung cancer is the second most diagnosed cancer after female breast cancer. Cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy and biopsy. However, to know the subtype of lung cancer based on the tissue type H and E staining is widely used, where the staining…
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；University of Cagliari（卡利亚里大学）
In this paper, we examine the strength of deep learning technique for diagnosing lung cancer on medical image analysis problem. Convolutional neural networks (CNNs) models become popular among the pattern recognition and computer vision research area because of their promising outcome on generating high-level image representations. We propose a new deep learning architecture for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks. We aim to learn discriminant compact features at beginning of our deep co…
MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
Computer Science and Engineering Department, NIIT University, Jaipur, India（尼特大学计算机科学与工程系，印度斋普尔）；CSE department, Christ (Deemed to be University), Bangalore, India（基督大学（认定为大学）计算机科学与工程系，印度班加罗尔）；Faculty of Computing, Engineering and the Built Environment, Birmingham City University, UK（伯明翰城市大学计算、工程与建筑环境学院，英国）
Lung and Colon cancer are one of the leading causes of mortality and morbidity in adults. Histopathological diagnosis is one of the key components to discern cancer type. The aim of the present research is to propose a computer aided diagnosis system for diagnosing squamous cell carcinomas and adenocarcinomas of lung as well as adenocarcinomas of colon using convolutional neural networks by evaluating the digital pathology images for these cancers. Hereby, rendering artificial intelligence as useful technology in the near future. A total of 2500 digital images were acquired from LC25000 datase…
Kamnoetvidya Science Academy（甘烹碧科学学院）；National Electronics and Computer Technology Center（泰国国家电子和计算机技术中心）；Vidyasirimedhi Institute of Science and Technology（瓦莱拉克科学技术研究院）
Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than normal chest x-rays, there is very limited access to these technologies in rural areas. Recently, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the tra…
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image datasets, it has become feasible to incorporate quantitative image features into survival prediction. So far, this kind of analysis is mostly based on radiomics features, i.e. a fixed set of features that is mathematically defined a priori. To capture highly abstract information, it is desirable to learn the feature extraction using convolutional neural networks. However, for tomographic medical images, model training…
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天科学与工程学院）；Eindhoven University of Technology（埃因霍温理工大学）；ScreenPoint Medical（ScreenPoint Medical 公司）；Pingan Technology（平安科技）；Lunit Inc.（Lunit 公司）；Queen Mary University of London（伦敦玛丽女王大学）；Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submi…
University of Technology Sydney（悉尼科技大学）；IBM Australia Limited（IBM澳大利亚有限公司）；Sejong University（世宗大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；Charles Sturt University（查尔斯特大学）
Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the black-box nature of deep learning models. Additionally, most lung nodules visible on chest X-ray are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both con…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Niskayuna High School（尼斯卡尤纳高中）
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieved an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identified patients with high CVD mortality risks (AUC of 0.768). We validated our model against ECG-gated cardiac CT based markers, includin…
Arkansas State University（阿肯色州立大学）；University of Michigan（密歇根大学）；Ann Arbor Algorithm（安阿伯算法公司）；University of Arkansas at Pine Bluff（阿肯色大学松崖分校）；University of Arkansas for Medical Sciences（阿肯色大学医学院）
Lung cancer is the leading cause of cancer deaths. Early detection through low-dose computed tomography (CT) screening has been shown to significantly reduce mortality but suffers from a high false positive rate that leads to unnecessary diagnostic procedures. Quantitative image analysis coupled to deep learning techniques has the potential to reduce this false positive rate. We conducted a computational analysis of 1449 low-dose CT studies drawn from the National Lung Screening Trial (NLST) cohort. We applied to this cohort our newly developed algorithm, DeepScreener, which is based on a nove…
University of Technology Sydney（悉尼科技大学）；University of Sydney（悉尼大学）；Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）
Lung cancer is the leading cause of cancer death worldwide. The critical reason for the deaths is delayed diagnosis and poor prognosis. With the accelerated development of deep learning techniques, it has been successfully applied extensively in many real-world applications, including health sectors such as medical image interpretation and disease diagnosis. By combining more modalities that being engaged in the processing of information, multimodal learning can extract better features and improve predictive ability. The conventional methods for lung cancer survival analysis normally utilize c…
Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey
College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, 610225, China（计算机科学与人工智能学院，西南民族大学）；The Australian National University, Canberra ACT 2601, Australia（澳大利亚国立大学）；School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China（计算机科学与技术学校，北京交通大学）；Hunan university of technology, Zhuzhou, 410072, China（湖南理工大学）；University of Adelaide, Adelaide, South Australia 5005, Australia（阿德莱德大学）；La Trobe University, Melbourne, Victoria 3086, Australia（拉筹伯大学）
Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans
Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University（斯坦福大学生物医学信息学研究中心（BMIR），医学系）；National Center for AI (NCAI), Saudi Data and AI Authority (SDAIA)（沙特数据与人工智能管理局国家人工智能中心（NCAI））；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"（乌克兰国立技术大学“伊戈尔·西科斯基基辅理工学院”）；Huizhou University（惠州学院）
The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (d…
James A. Haley Veterans’ Hospital（詹姆斯·A·黑利退伍军人医院）；University of South Florida（南佛罗里达大学）；Moffitt Cancer Center（莫菲特癌症中心）
The field of Machine Learning, a subset of Artificial Intelligence, has led to remarkable advancements in many areas, including medicine. Machine Learning algorithms require large datasets to train computer models successfully. Although there are medical image datasets available, more image datasets are needed from a variety of medical entities, especially cancer pathology. Even more scarce are ML-ready image datasets. To address this need, we created an image dataset (LC25000) with 25,000 color images in 5 classes. Each class contains 5,000 images of the following histologic entities: colon a…
We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiti…
In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans. Using less than 10,000 training examples, our deep networks perform two times better than a standard radiology software. Visualization of the networks' neurons reveals semantically meaningful features that are consistent with the clinical knowledge and radiologists' perception. Our paper also proposes a novel framework for rapidly adapting deep networks to the radiologists' feedback, or change in the data due to the shift in sensor's resolution or patient pop…
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation
School of Computer Science and Technology, Shandong University of Finance and Economics（山东财经大学计算机科学与技术学院）；School of Information Science and Technology, Linyi University（临沂大学信息科学与技术学院）；Thoracic Surgery Department of Linyi Central Hospital（临沂市中心医院胸外科）；Personnel Department of Linyi Central Hospital（临沂市中心医院人事科）
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection
Laboratoire LITAN École supérieure en Sciences et Technologies de l’Informatique et du Numérique（高等计算机与数字科学技术学院 LITAN 实验室）；College of Engineering and Information Technology, University of Dubai（迪拜大学工程与信息技术学院）；Department of Computer Science, University of Sharjah（沙迦大学计算机科学系）；Institute of Artificial Intelligence, De Montfort University（德蒙福特大学人工智能研究所）
University of Michigan（密歇根大学）；Imbio LLC（Imbio有限责任公司）
Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analys…
Classification based deep learning models for lung cancer and disease using medical images
Laboratory for Artificial Intelligence of Personalized Medicine, School of Artificial Intelligence, Guilin University of Electronic Technology（人工智能个性化医学实验室，人工智能学院，桂林电子科技大学）；The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Superieure（影像、视觉与人工智能实验室，蒙特利尔理工学院）
SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信工程学院）；College of Information Science and Engineering, Hunan Normal University（湖南师范大学信息科学与工程学院）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）
Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
Hunan University（湖南大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Central South University（中南大学）；Hunan Clinical Medical Research Center for Cancer Pathogenic Genes Testing and Diagnosis（湖南省肿瘤致病基因检测与诊断临床医学研究中心）；Shanghai Cancer Institute（上海市肿瘤研究所）；Renji Hospital, School of Medicine, Shanghai Jiaotong University（上海交通大学医学院附属仁济医院）；Shanghai Jiaotong University（上海交通大学）
Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration
Hatyaiwittayalai School（哈雅维泰莱学校）；School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology（Sirindhorn国际技术学院）；Thammasat University（泰国朱拉大学）；Pathum Thani, Thailand（帕亨他尼，泰国）
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Lung cancer is a leading cause of death worldwide. Early-stage detection of lung cancer is essential for a more favorable prognosis. Radiogenomics is an emerging discipline that combines medical imaging and genomics features for modeling patient outcomes non-invasively. This study presents a radiogenomics pipeline that has: 1) a novel mixed architecture (RA-Seg) to segment lung cancer through attention and recurrent blocks; and 2) deep feature classifiers to distinguish Epidermal Growth Factor Receptor (EGFR) mutation status. We evaluate the proposed algorithm on multiple public datasets to as…
Fairness Evaluation of Risk Estimation Models for Lung Cancer Screening
Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center（放射医学成像分析组，医学影像系，拉德堡德大学医学中心）；Department of Artificial Intelligence, Radboud University（人工智能系，拉德堡德大学）；Ethics of Healthcare Group, Department IQ Health, Radboud University Medical Center（医疗伦理组，IQ健康系，拉德堡德大学医学中心）
Southern Methodist University（南卫理公会大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Tumor shape is a key factor that affects tumor growth and metastasis. This paper proposes a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examines its effect on the time-to-event data. The proposed topological features are invariant to scale-preserving transformation and can summarize various tumor shape patterns. The topological features are represented in functional space and used as functional predictors in a functional Cox proportional hazards model. The proposed model enables interpretable inference ab…
University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage framework for WSI representation learning. We sample relevant patches using a color-based method and use graph neural networks to learn relations among sampled patches to aggregate the image information into a single vector representation. We introduce attention via graph pooling to automatically infer patches with higher relevance. We demonstrate the performance of our approach for discriminating two sub-types of lung ca…
Lung cancer is the leading cause of cancer-related deaths in the past several years. A major challenge in lung cancer screening is the detection of lung nodules from computed tomography (CT) scans. State-of-the-art approaches in automated lung nodule classification use deep convolutional neural networks (CNNs). However, these networks require a large number of training samples to generalize well. This paper investigates the use of capsule networks (CapsNets) as an alternative to CNNs. We show that CapsNets significantly outperforms CNNs when the number of training samples is small. To increase…
Institute for Infocomm Research（资讯通信研究院）；CentraleSupélec（巴黎中央理工-高等电力学院）；National Cancer Centre Singapore（新加坡国立癌症中心）；Hunan University（湖南大学）；Chesed Radiology（Chesed放射学）；Nanyang Technological University（南洋理工大学）
We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.
University of Waterloo（滑铁卢大学）；University of Guelph（圭尔夫大学）；Vector Institute（向量研究所）
Objective: Lung cancer is the leading cause of cancer-related death worldwide. Computer-aided diagnosis (CAD) systems have shown significant promise in recent years for facilitating the effective detection and classification of abnormal lung nodules in computed tomography (CT) scans. While hand-engineered radiomic features have been traditionally used for lung cancer prediction, there have been significant recent successes achieving state-of-the-art results in the area of discovery radiomics. Here, radiomic sequencers comprising of highly discriminative radiomic features are discovered directl…
University of Applied Sciences - Wiener Neustadt（维也纳新城应用科学大学）；LAAS-CNRS（法国国家科学研究中心 LAAS 实验室）；Univ de Toulouse（图卢兹大学）
Lung cancer accounts for the highest number of cancer deaths globally. Early diagnosis of lung nodules is very important to reduce the mortality rate of patients by improving the diagnosis and treatment of lung cancer. This work proposes an automated system to classify lung nodules as malignant and benign in CT images. It presents extensive experimental results using a combination of geometric and histogram lung nodule image features and different linear and non-linear discriminant classifiers. The proposed approach is experimentally validated on the LIDC-IDRI public lung cancer screening thor…
Dissecting the Dental Lung Cancer Axis via Mendelian Randomization and Mediation Analysis
Department of Second Dental Center, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China（上海交通大学医学院上海第九人民医院第二口腔科）；School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, China（复旦大学公共卫生学院）；Multimedia Laboratory, The Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学多媒体实验室）
We developed a deep learning framework that helps to automatically identify and segment lung cancer areas in patients' tissue specimens. The study was based on a cohort of lung cancer patients operated at the Uppsala University Hospital. The tissues were reviewed by lung pathologists and then the cores were compiled to tissue micro-arrays (TMAs). For experiments, hematoxylin-eosin stained slides from 712 patients were scanned and then manually annotated. Then these scans and annotations were used to train segmentation models of the developed framework. The performance of the developed deep lea…
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Klinikum Ingolstadt（英戈尔施塔特医院）；University of Regensburg（雷根斯堡大学）；University Hospital Regensburg（雷根斯堡大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very sma…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e.g., heavy smokers between 55 and 80 years old). The recommended screening practice significantly reduces all-cause mortality, but the vast majority of screening results are negative for cancer. If patients at very low risk could be identified based on individualized, image-based biomarkers, the health care resources could be more efficiently allocated to higher risk patients and reduce overall exposure to ionizing radiation. In this work, we propose a multi-task (diagnosi…
NMAM Institute of Technology（NMAM理工学院）；Visvesvaraya Technological University（韦斯韦斯瓦拉亚理工大学）；Sahyadri College of Engineering and Management（萨亚德里工程与管理学院）；JSS Academy of Technical Education（JSS技术教育学院）
Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we propose…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Vanderbilt Ingram Cancer Center（范德堡英格拉姆癌症中心）
Early detection of lung cancer is essential in reducing mortality. Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer among individuals selected based on very limited clinical information. However, this strategy yields high false positive rates, which can lead to unnecessary and potentially harmful procedures. To address such challenges, we established a pipeline that co-learns from detailed clinical demographics and 3D CT images. Toward this end, we leveraged data from the Consortium for Molecular and Cellular Characterization of S…
Philips Research（飞利浦研究院）；Human Longevity, Inc.（人类长寿公司）；University of Amsterdam（阿姆斯特丹大学）
Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been recently demonstrated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Objective: To compare the performance of a deep learning model to state-of-the-art automated algorithms and radiologists as well as assessing the robustness of the algorithm in heterogeneous datasets. Design, Setting, and Participants: Three low-dose CT lung cancer screening datasets from hetero…
With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer CT screening has been shown to reduce mortality by up to 40% and is now included in US screening guidelines. Reducing the high error rates in lung cancer screening is imperative because of the high clinical and financial costs caused by diagnosis mistakes. Despite the use of standards for radiological diagnosis, persistent inter-grader variability and incomplete characterization of comprehensive imaging findings remain as limitations of current methods. These limitat…
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
Chalmers University of Technology（查尔姆斯理工大学）；Umeå University（于默奥大学）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；Sahlgrenska Academy at Gothenburg University（哥德堡大学萨尔格伦斯卡学院）
Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers
Oak Ridge National Laboratory（橡树岭国家实验室）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Phoenix VA Medical Center（凤凰城退伍军人事务医疗中心）；Vanderbilt University Medical Center（范德堡大学医学中心）
National University of Defense Technology（中国人民解放军国防科技大学）；Central South University（中南大学）；Autonomous University of Barcelona（巴塞罗那自治大学）；Eindhoven University of Technology（埃因霍温理工大学）；The First Hospital of Changsha City（长沙市第一医院）；The First People’s Hospital of Foshan（佛山市第一人民医院）；Yale School of Medicine（耶鲁医学院）；Radboud University Medical Center（拉德堡德大学医学中心）
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform best in lung cancer diagnosis. Methods: Non-small cell lung carcinoma and small cell lung carcinoma biopsy specimens were consecutively obtained and stained. The specimen slides were diagnosed by two experienced pathologists (over 20 years). Several deep learning models were trained to discriminate cancer and non-cancer biopsies. Result: Deep learning models give reasonable AUC from 0.8810 to 0.9119. Conclusion: The de…
School of Information Technology（信息技术学院）；University of Cincinnati（辛辛那提大学）
Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode a…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Recently, multi-task networks have shown to both offer additional estimation capabilities, and, perhaps more importantly, increased performance over single-task networks on a "main/primary" task. However, balancing the optimization criteria of multi-task networks across different tasks is an area of active exploration. Here, we extend a previously proposed 3D attention-based network with four additional multi-task subnetworks for the detection of lung cancer and four auxiliary tasks (diagnosis of asthma, chronic bronchitis, chronic obstructive pulmonary disease, and emphysema). We introduce an…
