Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a type of deep learning, can removes such artifacts. Various architectures have been proposed for the CNNs, and the accuracy of artifact removal varies depending on the choice of architecture. Therefore, it is necessary to determine the artifact removal accuracy, depending on the selected architecture. In this study, we focus on endoscopic surgical instruments as artifacts, and determine and discuss the artifact removal accuracy using seven different CNN architectures.
Bournemouth University（伯恩茅斯大学）；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）；BCN Medtech（巴塞罗那医疗科技公司）；Universitat Pompeu Fabra（庞培法布拉大学）；ICREA（加泰罗尼亚高等研究院）
Polyps represent an early sign of the development of Colorectal Cancer. The standard procedure for their detection consists of colonoscopic examination of the gastrointestinal tract. However, the wide range of polyp shapes and visual appearances, as well as the reduced quality of this image modality, turn their automatic identification and segmentation with computational tools into a challenging computer vision task. In this work, we present a new strategy for the delineation of gastrointestinal polyps from endoscopic images based on a direct extension of common encoder-decoder networks for se…
A Graph-Augmented knowledge Distillation based Dual-Stream Vision Transformer with Region-Aware Attention for Gastrointestinal Disease Classification with Explainable AI
Department of Computer Science and Engineering（计算机科学与工程系）
A CNN-Based Blind Denoising Method for Endoscopic Images
Institute of Microelectronics, Tsinghua University（清华大学微电子学研究所）；Graduate School at Shenzhen, Tsinghua University（清华大学深圳国际研究生院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）
SimulaMet；UIT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）；Simula Research Laboratory（Simula研究实验室）；Oslo Metropolitan University（奥斯陆城市大学）；Sintef Digital（SINTEF数字研究所）；Augere Medical AS（Augere Medical公司）；Sahlgrenska University Hospital-Mölndal（萨尔格伦斯卡大学医院-默恩达尔）；Bærum Hospital（贝鲁姆医院）；Karolinska University Hospital（卡罗林斯卡大学医院）
Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the ``Kvasir-Instrument'' dataset which consists of $590$ annotated frames cont…
GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization
Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）；Manchester Metropolitan University（曼彻斯特城市大学）；Qatar University（卡塔尔大学）；Universiti Kebangsaan Malaysia（马来西亚国立大学）
A large number of different lesions and pathologies can affect the human digestive system, resulting in life-threatening situations. Early detection plays a relevant role in the successful treatment and the increase of current survival rates to, e.g., colorectal cancer. The standard procedure enabling detection, endoscopic video analysis, generates large quantities of visual data that need to be carefully analyzed by an specialist. Due to the wide range of color, shape, and general visual appearance of pathologies, as well as highly varying image quality, such process is greatly dependent on t…
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Northwestern University（西北大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Jefferson Health NJ（新泽西杰斐逊医疗）；SimulaMet（西穆拉梅特研究所）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Suzhou Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院苏州智能计算技术研究院）
Robotic-assisted surgery allows surgeons to conduct precise surgical operations with stereo vision and flexible motor control. However, the lack of 3D spatial perception limits situational awareness during procedures and hinders mastering surgical skills in the narrow abdominal space. Depth estimation, as a representative perception task, is typically defined as an image reconstruction problem. In this work, we show that depth estimation can be reformed from a 3D surface perspective. We propose a loss function for depth estimation that integrates the surface-aware constraints, leading to a fas…
Wireless Capsule Endoscopy (WCE) presented in 2001 as one of the key approaches to observe the entire gastrointestinal (GI) tract, generally the small bowels. It has been used to detect diseases in the gastrointestinal tract. Endoscopic image analysis is still a required field with many open problems. The quality of many images it produced is rather unacceptable due to the nature of this imaging system, which causes some issues to prognosticate by physicians and computer-aided diagnosis. In this paper, a novel technique is proposed to improve the quality of images captured by the WCE. More spe…
SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures
Intuitive Surgical, Inc.（Intuitive Surgical公司）；Johns Hopkins Medicine Neurosurgery（约翰霍普金斯医学神经外科）；Johns Hopkins University Electrical and Computer Engineering（约翰霍普金斯大学电气与计算机工程）；University of British Columbia Electrical and Computer Engineering（不列颠哥伦比亚大学电气与计算机工程）；Wilford & Kate Bailey Small Animal Teaching Hospital（威尔福德与凯蒂·贝利小动物教学医院）
PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Melbourne（墨尔本大学）；Chinese Academy of Sciences（中国科学院）
SimulaMet；UiT The Arctic University of Norway（挪威北极大学）；Oslo Metropolitan University（奥斯陆城市大学）；Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Bærum Hospital（贝鲁姆医院）；Augere Medical AS（Augere Medical 公司）；Sahlgrenska University Hospital-Mölndal Hospital（萨尔格伦斯卡大学医院-默恩达尔医院）；Sahlgrenska Academy, University of Gothenburg（哥德堡大学萨尔格伦斯卡学院）
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to de…
