Institute Mihajlo Pupin, University of Belgrade（贝尔格莱德大学米哈伊洛·普平研究所）；Center for Pathology and Forensics of the Military Medical Academy in Belgrade（贝尔格莱德军事医学科学院病理与法医中心）；University of Siegen（锡根大学）；Wireill；The School of Computing, University Union AB（AB大学联盟计算机学院）
Jelena Vasiljević, Ivica Milosavljević, Vladimir Krstić, Nataša Zivić, Lazar Berbakov, Luka Lopušina, Dhinaharan Nagamalai, Milutin Cerović
备注：
11 pages, 6 figures, 11th International Conference on Security and its Applications (CNSA 2018), Zurich, Switzerland,
发表信息：
Computer Science & Information Technology (CS & IT), 2018
This paper shows results of computer analysis of images in the purpose of finding differences between medical images in order of their classifications in terms of separation malign tissue from a normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability
PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT and Radiology Report Dataset for Medical Imaging Research
PET Center, Huashan Hospital, Fudan University（复旦大学附属华山医院PET中心）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；Shanghai Universal Medical Imaging Diagnostic Center（上海环球医学影像诊断中心）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）
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…
This study focuses on automatic skin cancer detection using a Meta-learning approach for dermoscopic images. The aim of this study is to explore the benefits of the generalization of the knowledge extracted from non-medical data in the classification performance of medical data and the impact of the distribution shift problem within limited data by using a simple class and distribution balancer algorithm. In this study, a small sample of a combined dataset from 3 different sources was used to fine-tune a ResNet model pre-trained on non-medical data. The results show an increase in performance …
MRI (Magnetic Resonance Imaging) is a technique used to analyze and diagnose the problem defined by images like cancer or tumor in a brain. Physicians require good contrast images for better treatment purpose as it contains maximum information of the disease. MRI images are low contrast images which make diagnoses difficult; hence better localization of image pixels is required. Histogram Equalization techniques help to enhance the image so that it gives an improved visual quality and a well defined problem. The contrast and brightness is enhanced in such a way that it does not lose its origin…
In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make…
In-context learning enables multimodal large language models to classify cancer pathology images
National Center for Tumor Diseases (NCT)（国家肿瘤中心）；Heidelberg University Hospital（海德堡大学医院）；Else Kroener Fresenius Center for Digital Health（埃尔瑟·克勒纳·弗赖森纽斯数字健康中心）；Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；Medical Faculty Mannheim, Heidelberg University（海德堡大学曼海姆医学院）；University Hospital Aachen（亚琛大学医院）；University Hospital Dresden（德累斯顿大学医院）
Neuromation；Massachusetts Institute of Technology（麻省理工学院）；Lyft Inc.（Lyft公司）；University of Michigan（密歇根大学）
Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge…
Cancer is one of the most common and fatal diseases in the world. Breast cancer affects one in every eight women and one in every eight hundred men. Hence, our prime target should be early detection of cancer because the early detection of cancer can be helpful to cure cancer effectively. Therefore, we propose a saliency detection system with the help of advanced deep learning techniques, such that the machine will be taught to emulate actions of pathologists for localization of diagnostically pertinent regions. We study identification of five diagnostic categories of breast cancer by training…
Survey on deep learning in multimodal medical imaging for cancer detection
Massey University（梅西大学）；Nantong University（南通大学）；Zhejiang Engineering Research Center of Intelligent Medicine（浙江省智能医学工程研究中心）；Jianpei Technology Co. Ltd（建培科技有限公司）
Korea Advanced Institute of Science and Technology（韩国高等科学技术研究院）；National Cancer Center（国家癌症中心）
Volumetric images from Magnetic Resonance Imaging (MRI) provide invaluable information in preoperative staging of rectal cancer. Above all, accurate preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment, as chemo-radiotherapy is usually recommended to patients with T3 (or greater) stage cancer. In this study, we present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes. Specifically, we propose 1) a custom ResNet-based volume …
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）
Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports
DAMO Academy, Alibaba Group（阿里巴巴集团达摩学院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Medical School, Shenzhen University（深圳大学医学院华南医院）
LLM-Guided Diagnostic Evidence Alignment for Medical Vision-Language Pretraining under Limited Pairing
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学阿利安斯商学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Microsoft Research（微软研究院）；TuSimple（图森未来）；Baidu Research（百度研究院）；Tencent Medical AI Labs（腾讯医疗AI实验室）；PingAn Tech（平安科技）
Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment recommendations require specially trained medical specialists. The current practice of reading medical images is labor-intensive, time-consuming, costly, and error-prone. It would be more desirable to have a computer-aided system that can automatically make diagnosis and treatment recommendations. Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images. In this t…
Expanding the Medical Decathlon dataset: segmentation of colon and colorectal cancer from computed tomography images
Neural Network Technologies Center（神经网络技术中心）；Institute of Urology and Human Reproductive Health（泌尿学与人类生殖健康研究所）；N.E. Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）；I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)（俄罗斯联邦卫生部I.M.谢切诺夫第一莫斯科国立医科大学（谢切诺夫大学））；Hospital of Faculty Surgery No. 2, UKB No. 4（第4大学临床医院第二外科教学医院）；University Clinical Hospital No. 2（第二大学临床医院）
本文通过为 Medical Decathlon 数据集增加经放射科医生验证的结直肠癌 CT 分割标记，并训练 UNet 模型进行 5 折交叉验证，以提升结直肠癌自动分割与早期检测能力。
Huron Digital Pathology（休伦数字病理学公司）；University of Waterloo（滑铁卢大学）；Vector Institute（矢量研究所）；McMaster University（麦克马斯特大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
The emergence of digital pathology has opened new horizons for histopathology and cytology. Artificial-intelligence algorithms are able to operate on digitized slides to assist pathologists with diagnostic tasks. Whereas machine learning involving classification and segmentation methods have obvious benefits for image analysis in pathology, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologist a novel approach to improve diagnostic accuracy through visual inspection of similar c…
Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and labor-intensive and difficulty in extracting high-quality features. Therefore, the paper proposes a computer-based feature fusion Convolutional neural network breast cancer image classification and detection method. The paper pre-trains two convolutional neural networks with different structures, and then uses the convolutional neural network to automatically extract the charact…
California Institute of Technology（加州理工学院）；Stanford University（斯坦福大学）
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural netw…
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…
Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification
Dept. of Electrical, Computer and Software Engineering（电气、计算机与软件工程系）；Ontario Tech University（安大略技术大学）；Dept. of Engineering（工程系）；Brock University（布鲁克大学）
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…
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医学中心）；New Hampshire Colonoscopy Registry（新罕布什尔结肠镜检查登记处）；White River Junction VA Medical Center（怀特河交界处退伍军人事务医疗中心）；Dartmouth College（达特茅斯学院）
While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish…
University of Toronto（多伦多大学）；Sunnybrook Research Institute（森尼布鲁克研究所）
Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI. These medical images contain rich information that can be explored to stratify patients within each stage group to further improve prognostic algorithms. Although the majority of cancer deaths result from metastatic and multifocal disease, building imaging biomarkers for patients with multiple tumors has been a challenging task due to the lack of annotated datasets and standard study framework. In this paper, we process two pu…
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）
Research Computing Center at the University of Chicago（芝加哥大学研究计算中心）
Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical practice. Still, developing computer-aided classification tools in prostate cancer diagnostics from multiparametric magnetic resonance images continues to be a challenge. In this work, we propose a novel deep learning approach for automatic classification of prostate lesions in the corresponding magnetic resonance images by constructing a two-stage multimodal multi…
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
Mansoura University（曼苏拉大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
Prostate cancer is the most dangerous cancer diagnosed in men worldwide. Prostate diagnosis has been affected by many factors, such as lesion complexity, observer visibility, and variability. Many techniques based on Magnetic Resonance Imaging (MRI) have been used for prostate cancer identification and classification in the last few decades. Developing these techniques is crucial and has a great medical effect because they improve the treatment benefits and the chance of patients' survival. A new technique that depends on MRI has been proposed to improve the diagnosis. This technique consists …
Deep learning methods, in particular convolutional neural networks, have emerged as a powerful tool in medical image computing tasks. While these complex models provide excellent performance, their black-box nature may hinder real-world adoption in high-stakes decision-making. In this paper, we propose an interactive system to take advantage of state-of-the-art interpretability techniques to assist radiologists with breast cancer screening. Our system integrates a deep learning model into the radiologists' workflow and provides novel interactions to promote understanding of the model's decisio…
New York University（纽约大学）；NYU School of Medicine（纽约大学医学院）；NYU Langone Medical Center（纽约大学朗格尼医学中心）
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differences between these two types of images. Specifically, fine details are necessary for detection in medical images, unlike in natural images where coarse structures matter most. This difference makes it inadequate to use the existing network architectures developed for natural imag…
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…
Johns Hopkins University（约翰霍普金斯大学）；Applied Physics Laboratory（应用物理实验室）；University of Virginia（弗吉尼亚大学）
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic. In contrast to most recent work in privacy-aware ML that has been focused on model alteration and post-processing steps, we propose here a novel and complementary scheme that enhances the security of medical data by controlling the data sharing process. We develop and evaluate a privacy defense protoc…
George Washington University（乔治华盛顿大学）；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
This paper constructs a novel intelligent medical diagnosis system, which can realize automatic communication and breast cancer pathological image recognition. This system contains two main parts, including a pre-training chatbot called M-Chatbot and an improved neural network model of EfficientNetV2-S named EfficientNetV2-SA, in which the activation function in top layers is replaced by ACON-C. Using information retrieval mechanism, M-Chatbot instructs patients to send breast pathological image to EfficientNetV2-SA network, and then the classifier trained by transfer learning will return the …
Queen’s University（女王大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Alberta（阿尔伯塔大学）；Exact Imaging（Exact影像）
Purpose: Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound (μUS) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from μUS, along with its first prospective validation. Methods: ProstNFound+ incorporates a medical FM, adapter tuning, and a custom prompt encoder that embeds PCa-specific clinical biomarkers. The model generates a cancer heatmap and a risk score for clinically significant PCa. Following training on multi…
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Meta Platforms Inc.（元平台公司）
Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry
Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；Karolinska Institutet（卡罗林斯卡学院）；Synlab；University of Bergen（卑尔根大学）；Griffith University（格里菲斯大学）；SciLifeLab
MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis
The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院）；Jiangsu Province Hospital（江苏省医院）；Huazhong University of Science Technology（华中科技大学）；Fudan University（复旦大学）
Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds fur…
Deformable MRI Sequence Registration for AI-based Prostate Cancer Diagnosis
Radboudumc（拉德堡德大学医学中心）；Fraunhofer MEVIS（弗劳恩霍夫梅维斯研究所）；Universität zu Lübeck（吕贝克大学）；University Medical Center Groningen（格罗宁根大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；Norwegian University of Science and Technology（挪威科技大学）
