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 original information and the brightness is preserved. We compare the different equalization techniques in this paper; the techniques are critically studied and elaborated. They are also tabulated to compare various parameters present in the image. In addition we have also segmented and extracted the tumor part out of the brain using K-means algorithm. For classification and feature extraction the method used is Support Vector Machine (SVM). The main goal of this research work is to help the medical field with a light of image processing.
Enhancement of human vision to get an insight to information content is of vital importance. The traditional histogram equalization methods have been suffering from amplified contrast with the addition of artifacts and a surprising unnatural visibility of the processed images. In order to overcome these drawbacks, this paper proposes interative, mean, and multi-threshold selection criterion with plateau limits, which consist of histogram segmentation, clipping and transformation modules. The histogram partition consists of multiple thresholding processes that divide the histogram into two part…
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）
The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions. This may impact the majority of radiomic studies published recently and contribute to issues regarding poor reproducibility of radiomic-based machine learning that has led to significant efforts for data harmonization; however, we believe the issues highlighted here are comparatively neglected, but often remedied by choosing static binning. The field of radiomics has improved through the development of community standards and open-source libr…
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）
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
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 …
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…
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）
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）
Indian Institute of Technology Delhi（德里印度理工学院）；Postgraduate Institute of Medical Education & Research（研究生医学教育与研究学院）
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined using a contemporary transformer architecture for highly accurate Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our experiments indicate that the detection accuracy of our model beats even human radiologists, and advocates its use as the second reader for GBC diag…
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran；Department of Computer Engineering Techniques, Mazaya University College, Nasiriyah, Iraq；Department of Medicine, Danube Private University, Krems an der Donau, Austria；Austrian Center for Medical Innovation；Research Center for Medical Image Analysis；Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
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…
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）
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…
Concordia University（康考迪亚大学）；University of Toronto（多伦多大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）
Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in "Radiomics". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of …
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。
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…
University Joseph Fourier（约瑟夫·傅里叶大学）；IPAL, CNRS UMI 2955（IPAL，法国国家科学研究中心UMI 2955）；CoSMo Software（CoSMo软件公司）；Temasys Communications（Temasys通信公司）；University Pierre and Marie Curie（皮埃尔和玛丽·居里大学）
Accurate detection of mitosis plays a critical role in breast cancer histopathology. Manual detection and counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Multispectral imaging is a recent medical imaging technology, proven successful in increasing the segmentation accuracy in other fields. This study aims at improving the accuracy of mitosis detection by developing a specific solution using multispectral and multifocal imaging of breast cancer histopathological data. We propose to enable clinical routine-compliant quality of mitosis discrimination…
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…
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…
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…
Indian Institute of Science Education and Research Kolkata（印度科学教育与研究所加尔各答）；Institute of Engineering & Management Kolkata（工程与管理学院加尔各答）；Indian Institute of Technology Kanpur（印度理工学院坎普尔）
Our proposed hyper kurtosis based histogram equalized DIC images enhances the contrast by preserving the brightness. The evolution and development of precancerous activity among tissues are studied through S-transform (ST). The significant variations of amplitude spectra can be observed due to increased medium roughness from normal tissue were observed in time-frequency domain. The randomness and inhomogeneity of the tissue structures among human normal and different grades of DIC tissues is recognized by ST based timefrequency analysis. This study offers a simpler and better way to recognize …
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）
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）
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）
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…
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-of-the-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most …
A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field
Indian Institute of Information Technology（印度信息技术学院）
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Features learned from single radiologic images are unable to provide information about whether and how much a lesion may be changing over time. Time-dependent features computed from repeated images can capture those changes and help identify malignant lesions by their temporal behavior. However, longitudinal medical imaging presents the unique challenge of sparse, irregular time intervals in data acquisition. While self-attention has been shown to be a versatile and efficient learning mechanism for time series and natural images, its potential for interpreting temporal distance between sparse,…
Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach
Faculty of Computing（计算学院）；Universiti Teknologi Malaysia（技术大学）；Faculty of Science and Technology（科学与技术学院）；Universitas Muhammadiyah Sidoarjo（穆罕默德大学西多亚罗乔）；Department of Information System（信息系统系）；Universitas Siber Jakarta（西巴克大学）
