Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Sun Yat-sen University（中山大学）；The Chinese University of Hong Kong(Shenzhen)（香港中文大学（深圳））；Longgang District People’s Hospital of Shenzhen（深圳市龙岗区人民医院）
Qin Wang, Hui Che, Weizhen Ding, Li Xiang, Guanbin Li, Zhen Li, Shuguang Cui
Differentiation of colorectal polyps is an important clinical examination. A computer-aided diagnosis system is required to assist accurate diagnosis from colonoscopy images. Most previous studies at-tempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. However, the wide range of these models' applications for clinical work has been limited by the lagging of imaging techniques. Thus, we propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification (CPC) through directly using white-light (WL) colonoscopy images in the examination. In practice, during training, the auxiliary NBI images are utilized to train a teacher network and guide the student network to acquire richer feature representation from WL images. The feature transfer is realized by domain alignment and contrastive learning. Eventually the final student network has the ability to extract aligned features from only WL images to facilitate the CPC task. Besides, we release the first public-available paired CPC dataset containing WL-NBI pairs for the alignment training. Quantitative and qualitative evaluation indicates that the proposed method outperforms the previous methods in CPC, improving the accuracy by 5.6%with very fast speed.
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Northwestern University（西北大学）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；The University of Gothenburg（哥德堡大学）；Harvard University（哈佛大学）；University Hospital Würzburg（维尔茨堡大学医院）；Hanoi Medical University（河内医科大学）；The Institute of Gastroenterology and Hepatology, Hanoi, Vietnam（越南河内胃肠病与肝病研究所）；Ho Chi Minh University Medical Center（胡志明市大学医疗中心）
本文发布涵盖三国三中心、五种成像模态的 3934 张息肉图像数据集 PolypDB，并提供分割、检测与联邦学习基准，以支持结肠镜 AI 算法开发。
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-…
Computer Vision Center（计算机视觉中心）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）；Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；Université de Montréal（蒙特利尔大学）；École Polytechnique de Montréal（蒙特利尔理工学院）；Hospital Clinic（临床医院）；Universidad de Barcelona（巴塞罗那大学）；Imagia Inc.（Imagia公司）
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extend…
The University of Kansas（堪萨斯大学）；The University of Kansas Medical Center（堪萨斯大学医学中心）；Washington University School of Medicine（华盛顿大学医学院）；Ryerson University（瑞尔森大学）
Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to trai…
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-…
Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Nanjing University of Science and Technology（南京理工大学）
The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, whic…
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Singapore Management University（新加坡管理大学）
Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps. Consequently, they often have high detection errors, especially on challenging polyp cases (e.g., small, flat, or partially visible polyps). In this work, we formulate polyp detection as a weakly-supervised anomaly detection task that uses video-level labelled training data to detect frame-level polyps. In particular, we propose a novel convolutional transforme…
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…
University of Central Florida（中佛罗里达大学）；Mayo Clinic（妙佑医疗国际（梅奥诊所））；University of Catania（卡塔尼亚大学）
Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the resection and histopathological analysis of polyps due to test accuracy and sensitivity of optical biopsy methods falling substantially below recommended levels. In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps. Our proposed method introduces several technical novelties including a novel capsule architecture with a capsule-average …
ERCPMP: An Endoscopic Image and Video Dataset for Colorectal Polyps Morphology and Pathology
Gastroenterology and Liver Disease Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学胃肠病学与肝病研究所胃肠病学与肝病研究中心）；Alborz University of Medical Sciences（厄尔布尔士医科大学）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；Iran University of Medical Sciences（伊朗医科大学）
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)…
RRTS Dataset: A Benchmark Colonoscopy Dataset from Resource-Limited Settings for Computer-Aided Diagnosis Research
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh（生物医学工程系，孟加拉国工程大学）；Dhaka Medical College, Dhaka, Bangladesh（达卡医学学院）
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…
REAL-Colon: A dataset for developing real-world AI applications in colonoscopy
Cosmo Intelligent Medical Devices（科斯莫智能医疗设备公司）；Ospedale dei Castelli (N.O.C.)（卡斯特利医院（N.O.C.））；University Hospital of St. Pölten（圣珀尔滕大学医院）；Humanitas University（乌纳塔斯大学）；Humanitas Clinical and Research Center IRCCS（乌纳塔斯临床与研究中心 IRCCS）；Sano Hospital（佐野医院）；Milan Center for Neuroscience, University of Milano–Bicocca（米兰-比可卡大学米兰神经科学中心）
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…
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…
We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, more than 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image a…
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
University of Concordia（康考迪亚大学）；University of Montreal（蒙特利尔大学）；Sunnybrook Health Science Centre（桑尼布鲁克健康科学中心）；Kingston General Hospital（金斯顿总医院）；Mila–Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Xiangya Hospital of Central South University（中南大学湘雅医院）
Colorectal polyps are abnormal tissues growing on the intima of the colon or rectum with a high risk of developing into colorectal cancer, the third leading cause of cancer death worldwide. Early detection and removal of colon polyps via colonoscopy have proved to be an effective approach to prevent colorectal cancer. Recently, various CNN-based computer-aided systems have been developed to help physicians detect polyps. However, these systems do not perform well in real-world colonoscopy operations due to the significant difference between images in a real colonoscopy and those in the public …
The Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth College（达特茅斯学院）
Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The propos…
University of Lincoln（林肯大学）；Lingnan Normal University（岭南师范学院）
Besides the complex nature of colonoscopy frames with intrinsic frame formation artefacts such as light reflections and the diversity of polyp types/shapes, the publicly available polyp segmentation training datasets are limited, small and imbalanced. In this case, the automated polyp segmentation using a deep neural network remains an open challenge due to the overfitting of training on small datasets. We proposed a simple yet effective polyp segmentation pipeline that couples the segmentation (FCN) and classification (CNN) tasks. We find the effectiveness of interactive weight transfer betwe…
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（胃肠病学与肝病学研究所）；Vietnam National University of Agriculture（越南国立农业大学）
Colorectal cancer is among the most common malignancies and can develop from high-risk colon polyps. Colonoscopy is an effective screening tool to detect and remove polyps, especially in the case of precancerous lesions. However, the missing rate in clinical practice is relatively high due to many factors. The procedure could benefit greatly from using AI models for automatic polyp segmentation, which provide valuable insights for improving colon polyp detection. However, precise segmentation is still challenging due to variations of polyps in size, shape, texture, and color. This paper propos…
The University of Adelaide（阿德莱德大学）；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）
Anomaly detection methods generally target the learning of a normal image distribution (i.e., inliers showing healthy cases) and during testing, samples relatively far from the learned distribution are classified as anomalies (i.e., outliers showing disease cases). These approaches tend to be sensitive to outliers that lie relatively close to inliers (e.g., a colonoscopy image with a small polyp). In this paper, we address the inappropriate sensitivity to outliers by also learning from inliers. We propose a new few-shot anomaly detection method based on an encoder trained to maximise the mutua…
Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer. Polyps are seen as protrusions on the colon wall and haustral folds, visible in the VC fly-through videos. A complete review of the colon surface requires full navigation from the rectum to the cecum in antegrade and retrograde directions, which is a tedious task that takes an average of 30 minutes. Crowdsourcing is a technique for non-expert users to perform certain tasks, such as image or video ann…
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
School of Basic Medical Sciences, Fudan University（复旦大学基础医学院）；Shanghai Key Laboratory of MICCAI（上海市MICCAI重点实验室）；Data Science Institute, Imperial College London（伦敦帝国理工学院数据科学研究所）；Shanghai Collaborative Innovation Centre of Endoscopy（上海市内镜微创技术协同创新中心）；Endoscopy Centre and Endoscopy Research Institute, Zhongshan Hospital, Fudan University（复旦大学附属中山医院内镜中心及内镜研究所）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）
University College London（伦敦大学学院）；University College Hospital（伦敦大学学院医院）
Potentially precancerous polyps detected with CT colonography (CTC) need to be removed subsequently, using an optical colonoscope (OC). Due to large colonic deformations induced by the colonoscope, even very experienced colonoscopists find it difficult to pinpoint the exact location of the colonoscope tip in relation to polyps reported on CTC. This can cause unduly prolonged OC examinations that are stressful for the patient, colonoscopist and supporting staff. We developed a method, based on monocular 3D reconstruction from OC images, that automatically matches polyps observed in OC with poly…
University of Oxford（牛津大学）；Oxford National Institute for Health Research Biomedical Research Centre（牛津国家健康研究所生物医学研究中心）；Arab Academy for Science and Technology（阿拉伯科学技术学院）；SimulaMet；UiT The Arctic University of Norway（挪威北极大学）；Middle East Technical University（中东技术大学）；City University of Hong Kong（香港城市大学）；Universitat Pompeu Fabra（庞培法布拉大学）
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realisin…
Instituto de Ingeniería Eléctrica, Universidad de la República（共和国大学电气工程研究所）；Duke University（杜克大学）
Computer tomographic colonography, combined with computer-aided detection, is a promising emerging technique for colonic polyp analysis. We present a complete pipeline for polyp detection, starting with a simple colon segmentation technique that enhances polyps, followed by an adaptive-scale candidate polyp delineation and classification based on new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The proposed system is tested with ground truth data, including flat and small polyps which are hard to detect ev…
SimulaMet；UiT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）；Oxford NIHR Biomedical Research Centre（牛津NIHR生物医学研究中心）；Oslo Metropolitan University（奥斯陆城市大学）
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible…
Real-time and robust automatic detection of polyps from colonoscopy videos are essential tasks to help improve the performance of doctors during this exam. The current focus of the field is on the development of accurate but inefficient detectors that will not enable a real-time application. We advocate that the field should instead focus on the development of simple and efficient detectors that an be combined with effective trackers to allow the implementation of real-time polyp detectors. In this paper, we propose a Kalman filtering tracker that can work together with powerful, but efficient…
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a leading cause of cancer deaths in the United States. Colorectal polyps that grow on the intima of the colon or rectum is an important precursor for CRC. Currently, the most common way for colorectal polyp detection and precancerous pathology is the colonoscopy. Therefore, accurate colorectal polyp segmentation during the colonoscopy procedure has great clinical significance in CRC early detection and prevention. In this paper, we propose a novel end-to-end deep learning framework for the colorectal polyp segmentation. …
RIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；South China University of Technology（华南理工大学）；CyPhi( Ψ Φ \Psi\Phi ) AI Research Lab, School of IT, Monash University Malaysia（马来西亚莫纳什大学信息技术学院CyPhi( Ψ Φ \Psi\Phi )人工智能研究实验室）
SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（华中科技大学武汉光电国家研究中心）；School of Engineering and Science, Huazhong University of Science and Technology（华中科技大学工程科学学院）；Huazhong University of Science and Technology Tongji Medical College（华中科技大学同济医学院）
Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
Department of Mathematics, University of Padova, Padova, Italy（帕多瓦大学数学系）；LTCI, Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France（巴黎电信学院）；Cosmo Intelligent Medical Devices, Dublin, Ireland（都柏林智能医疗设备公司）
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Longgang District People’s Hospital of Shenzhen（深圳市龙岗区人民医院）
The colorectal polyps classification is a critical clinical examination. To improve the classification accuracy, most computer-aided diagnosis algorithms recognize colorectal polyps by adopting Narrow-Band Imaging (NBI). However, the NBI usually suffers from missing utilization in real clinic scenarios since the acquisition of this specific image requires manual switching of the light mode when polyps have been detected by using White-Light (WL) images. To avoid the above situation, we propose a novel method to directly achieve accurate white-light colonoscopy image classification by conductin…
We present a neural network architecture for medical image segmentation of diabetic foot ulcers and colonoscopy polyps. Diabetic foot ulcers are caused by neuropathic and vascular complications of diabetes mellitus. In order to provide a proper diagnosis and treatment, wound care professionals need to extract accurate morphological features from the foot wounds. Using computer-aided systems is a promising approach to extract related morphological features and segment the lesions. We propose a convolution neural network called HarDNet-DFUS by enhancing the backbone and replacing the decoder of …
Temporally-Aware Supervised Contrastive Learning for Polyp Counting in Colonoscopy
Department of Mathematics, University of Padova, Padova, Italy（帕多瓦大学数学系）；Cosmo Intelligent Medical Devices, Dublin, Ireland（科莫智能医疗设备公司）；Milan Center for Neuroscience, University of Milano–Bicocca, Milan, Italy（米兰神经科学中心）
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…
Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学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）；Austin Health（奥斯汀健康中心）；Lyell McEwin Hospital（莱尔麦克尤恩医院）
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulated as a few-shot anomaly classification problem, where the training set is highly imbalanced with the large majority of frames consisting of normal images and a small minority comprising frames with polyps. Colonoscopy videos may contain blurry images and frames displaying feces and water jet sprays to clean the colon -- such frames can mistakenly be detected as anomalies, so we have implemented a classifier to reject…
