This paper is created to explore deep learning models and algorithms that results in highest accuracy in detecting polyp on colonoscopy images. Previous studies implemented deep learning using convolution neural network (CNN) algorithm in detecting polyp and non-polyp. Other studies used dropout, and data augmentation algorithm but mostly not checking the overfitting, thus, include more than four-layer modelss. Rulei Yu et.al from the Institute of Software, Chinese Academy of Sciences said that transfer learning is better talking about performance or improving the previous used algorithm. Most especially in applying the transfer learning in feature extraction. Series of experiments were conducted with only a minimum of 4 CNN layers applying previous used models and identified the model that produce the highest percentage accuracy of 98% among the other models that apply transfer learning. Further studies could use different optimizer to a different CNN modelsto increase accuracy.
University of Bergen（卑尔根大学）；SimulaMet（西穆拉梅特）；Nepal Medical College（尼泊尔医学院）；Kathmandu University（加德满都大学）；UiT The Arctic University of Norway（挪威北极大学）；University of Oslo（奥斯陆大学）；Oslo Metropolitan University（奥斯陆城市大学）
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' p…
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）
Colorectal cancer is one of fatal cancer worldwide. Colonoscopy is the standard treatment for examination, localization, and removal of colorectal polyps. However, it has been shown that the miss-rate of colorectal polyps during colonoscopy is between 6 to 27%. The use of an automated, accurate, and real-time polyp segmentation during colonoscopy examinations can help the clinicians to eliminate missing lesions and prevent further progression of colorectal cancer. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build a fast segmenta…
This paper aims to contribute in bench-marking the automatic polyp segmentation problem using generative adversarial networks framework. Perceiving the problem as an image-to-image translation task, conditional generative adversarial networks are utilized to generate masks conditioned by the images as inputs. Both generator and discriminator are convolution neural networks based. The model achieved 0.4382 on Jaccard index and 0.611 as F2 score.
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Indian Statistical Institute（印度统计研究所）；Northwestern University（西北大学）
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorectal polyp varies significantly among the endoscopists. It is well known that a computer-aided diagnosis (CAD) system can assist endoscopists in detecting colon polyps and minimize the variation among endoscopists. In this study, we introduce a novel deep learning architecture, named MKDCNet, for automatic polyp segmentation robust to significant changes in polyp data distribution. MKDCNet is simply an encoder-decoder n…
SimulaMet（西穆拉大都会研究所）；UiT The Arctic University of Norway（挪威UiT北极大学）；Oslo Metropolitan University（奥斯陆城市大学）；Augere Medical AS（奥杰雷医疗公司）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；Bærum Hospital（贝鲁姆医院）
Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the over…
SimulaMet（西穆拉梅特研究所）；UIT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）；Oslo Metropolitan University（奥斯陆城市大学）
Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions)…
University of Saskatchewan（萨斯喀彻温大学）；University of Stanford（斯坦福大学）
Colorectal cancer is the third most common cancer-related death after lung cancer and breast cancer worldwide. The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy. Computer-aided diagnosis systems have the potential to be applied for polyp screening and reduce the number of missing polyps. In this paper, we compare the performance of different deep learning architectures as feature extractors, i.e. ResNet, DenseNet, InceptionV3, InceptionResNetV2 and SE-ResNeXt in the encoder part of a U-Net architecture. We validated the performance of p…
Women’s Hospital School of Medicine, ZheJiang University（浙江大学医学院附属妇产科医院）；Affiliated Hospital of Zhejiang University（浙江大学附属医院）
Endoscopic diagnosis is an important means for gastric polyp detection. In this paper, a panoramic image of gastroscopy is developed, which can display the inner surface of the stomach intuitively and comprehensively. Moreover, the proposed automatic detection solution can help doctors locate the polyps automatically, and reduce missed diagnosis. The main contributions of this paper are: firstly, a gastroscopic panorama reconstruction method is developed. The reconstruction does not require additional hardware devices, and can solve the problem of texture dislocation and illumination imbalance…
Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Norwegian University of Science and Technology（挪威科技大学）；OmniVision Technologies Norway AS（OmniVision技术挪威公司）；LG CNS
Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extr…
University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Nagoya University（名古屋大学）
The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps -- such training set is con…
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-…
Hanoi University of Science and Technology（河内科技大学）；Hanoi Medical University（河内医科大学）；The Institute of Gastroenterology and Hepatology（胃肠肝病学研究所）；University of Medicine and Pharmacy, Hue University（顺化大学医药大学）；Vietnam National University of Agriculture（越南国立农业大学）
Automatic polyp segmentation has proven to be immensely helpful for endoscopy procedures, reducing the missing rate of adenoma detection for endoscopists while increasing efficiency. However, classifying a polyp as being neoplasm or not and segmenting it at the pixel level is still a challenging task for doctors to perform in a limited time. In this work, we propose a fine-grained formulation for the polyp segmentation problem. Our formulation aims to not only segment polyp regions, but also identify those at high risk of malignancy with high accuracy. In addition, we present a UNet-based neur…
Norwegian University of Science and Technology (NTNU)（挪威科技大学（NTNU））；Oslo University Hospital（奥斯陆大学医院）；University of Oslo (UiO)（奥斯陆大学（UiO））
One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in a generative adversarial networks approach. We propose an edge filtering-based combined input conditioned image to train our proposed networks. This enable…
University of Science, VNU-HCM（胡志明市国家大学科学大学）；John von Neumann Institute, VNU-HCM（胡志明市国家大学约翰·冯·诺依曼研究所）；Vietnam National University, Ho Chi Minh city（越南国家大学胡志明市分校）
The Medico: Multimedia Task 2020 focuses on developing an efficient and accurate computer-aided diagnosis system for automatic segmentation [3]. We participate in task 1, Polyps segmentation task, which is to develop algorithms for segmenting polyps on a comprehensive dataset. In this task, we propose methods combining Residual module, Inception module, Adaptive Convolutional neural network with U-Net model, and PraNet for semantic segmentation of various types of polyps in endoscopic images. We select 5 runs with different architecture and parameters in our methods. Our methods show potential…
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Larkin Community Hospital Palm Springs Campus（拉金社区医院棕榈泉院区）；Long Island Jewish Valley Stream（长岛犹太谷溪医院）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）
Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early detection. Colonoscopy is the primary modality used to diagnose colon cancer. However, the miss rate of polyps, adenomas and advanced adenomas remains significantly high. Early detection of polyps at the precancerous stage can help reduce the mortality rate and the economic burden associated with colorectal cancer. Deep learning-based computer-aided diagnosis (CADx) system may help gastroenterologists to identify po…
Fudan University（复旦大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）；Huashan Hospital（华山医院）
Automatic colorectal polyp detection in colonoscopy video is a fundamental task, which has received a lot of attention. Manually annotating polyp region in a large scale video dataset is time-consuming and expensive, which limits the development of deep learning techniques. A compromise is to train the target model by using labeled images and infer on colonoscopy videos. However, there are several issues between the image-based training and video-based inference, including domain differences, lack of positive samples, and temporal smoothness. To address these issues, we propose an Image-video-…
National Institute of Standards and Technology（美国国家标准与技术研究院）；Colorado State University（科罗拉多州立大学）；University of Colorado（科罗拉多大学）；Hanoi University of Science and Technology（河内科技大学）；Hanoi Medical University（河内医科大学）；Institute of Gastroenterology and Hepatology（胃肠病学与肝病学研究所）；University of Medicine and Pharmacy, Hue University（顺化大学医学与药学院）；Vietnam National University of Agriculture（越南国立农业大学）
In recent years, computer-aided automatic polyp segmentation and neoplasm detection have been an emerging topic in medical image analysis, providing valuable support to colonoscopy procedures. Attentions have been paid to improving the accuracy of polyp detection and segmentation. However, not much focus has been given to latency and throughput for performing these tasks on dedicated devices, which can be crucial for practical applications. This paper introduces a novel deep neural network architecture called BlazeNeo, for the task of polyp segmentation and neoplasm detection with an emphasis …
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Northwestern University（西北大学）；Jadavpur University（贾达普大学）；Technische Universität Hamburg（汉堡工业大学）；SimulaMet（西穆拉梅特研究所）；Julius-Maximilian University of Würzburg（维尔茨堡大学）；Australian National University（澳大利亚国立大学）；Gachon University（嘉泉大学）；University of Oxford（牛津大学）；University of Bergen（卑尔根大学）；University of Oslo（奥斯陆大学）；Swedish Medical Center（瑞典医疗中心）；National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；University of Science, VNU-HCM（胡志明市国家大学科学大学）
University of Barcelona（巴塞罗那大学）；University Hospital Vall d’Hebron（希伯伦谷大学医院）
Wireless capsule endoscopy is a medical procedure used to visualize the entire gastrointestinal tract and to diagnose intestinal conditions, such as polyps or bleeding. Current analyses are performed by manually inspecting nearly each one of the frames of the video, a tedious and error-prone task. Automatic image analysis methods can be used to reduce the time needed for physicians to evaluate a capsule endoscopy video, however these methods are still in a research phase. In this paper we focus on computer-aided polyp detection in capsule endoscopy images. This is a challenging problem because…
South Central University for Nationalities（中南民族大学）；Nanjing Eastern Theater General Hospital（南京东部战区总医院）
Wireless Capsule Endoscopy(WCE) has been widely used for the screening of gastrointestinal(GI) diseases, especially the small intestine, due to its advantages of non-invasive and painless imaging of the entire digestive tract.However, the huge amount of image data captured by WCE makes manual reading a process that requires a huge amount of tasks and can easily lead to missed detection and false detection of lesions.Therefore, In this paper, we propose a \textbf{T}wo-stage \textbf{M}ulti-scale \textbf{F}eature-fusion learning network(\textbf{TMFNet}) to automatically detect small intestinal po…
