標(biāo)題: Titlebook: Computer-Aided Analysis of Gastrointestinal Videos; Jorge Bernal,Aymeric Histace Book 2021 Springer Nature Switzerland AG 2021 Computer Vi [打印本頁(yè)] 作者: interleukins 時(shí)間: 2025-3-21 19:00
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos影響因子(影響力)
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos影響因子(影響力)學(xué)科排名
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos被引頻次
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos被引頻次學(xué)科排名
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos年度引用
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos年度引用學(xué)科排名
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos讀者反饋
書(shū)目名稱Computer-Aided Analysis of Gastrointestinal Videos讀者反饋學(xué)科排名
作者: 改變 時(shí)間: 2025-3-21 20:49
https://doi.org/10.1007/978-90-6704-743-2 time dependencies than traditional RNNs and it is a convolutional variant of these LSTM models?(Xingjian et?al. .) that is used in this chapter. The latter can not only encode temporal features but can simultaneously incorporate spatial features into one single layer.作者: 免除責(zé)任 時(shí)間: 2025-3-22 01:19 作者: obligation 時(shí)間: 2025-3-22 07:24
Computer-Aided Analysis of Gastrointestinal Videos作者: 使虛弱 時(shí)間: 2025-3-22 10:07
Computer-Aided Analysis of Gastrointestinal Videos978-3-030-64340-9作者: 分散 時(shí)間: 2025-3-22 13:56 作者: 分散 時(shí)間: 2025-3-22 18:04
https://doi.org/10.1007/978-3-030-64340-9Computer Vision; Colorectal Cancer; Videocolonoscopy; Wireless Capsule Endoscopy; Intestinal Pathologies作者: foliage 時(shí)間: 2025-3-23 00:40
978-3-030-64342-3Springer Nature Switzerland AG 2021作者: 憂傷 時(shí)間: 2025-3-23 02:43 作者: 絕種 時(shí)間: 2025-3-23 07:40 作者: Rankle 時(shí)間: 2025-3-23 12:22 作者: Airtight 時(shí)間: 2025-3-23 16:09 作者: 桶去微染 時(shí)間: 2025-3-23 21:52
Delphine Allès,Pascal Vennessongh transfer learning. However, in this work, we propose a deep learning architecture that exploits fine-tuning and random initialization of weights in a multi-encoder with a single decoder network architecture.作者: 祖?zhèn)?nbsp; 時(shí)間: 2025-3-23 22:46 作者: Heart-Attack 時(shí)間: 2025-3-24 02:40 作者: 鋼盔 時(shí)間: 2025-3-24 10:18 作者: 金絲雀 時(shí)間: 2025-3-24 11:14
The European VC-Funded Startup Guideor a variety of problems?(Tan et?al. .; Chen et?al. .; Habibzadeh et?al. .; Putten et?al. .). Additionally, we incorporate multi-scale information in our approach by training models with different input image resolutions. This approach is taken since multi-scale approaches have been shown to be effe作者: 進(jìn)入 時(shí)間: 2025-3-24 15:18
https://doi.org/10.1007/978-90-6704-743-2ysis (Wang et?al. .; Urban et?al. .; Shin et?al. .; Mohammed et?al. .). Colonoscopy, however, is a video-based modality and an endoscopist will always use the contextual information from previous frames to make an accurate decision about the potential presence of a polyp. Recent developments in sema作者: 注意力集中 時(shí)間: 2025-3-24 19:22
The European Union’s Pivot to AfricaWireless Capsule Endoscopy (WCE) takes the form of a pill equipped with a CCD or CMOS sensor, two batteries, and a RF (radiofrequency) transmitter that enables the wireless identification of gastrointestinal abnormalities such as ulcers, blood, and polyps (Moglia et?al. .) with no need for hospitalization or sedation.作者: 黃油沒(méi)有 時(shí)間: 2025-3-25 02:59 作者: Petechiae 時(shí)間: 2025-3-25 04:55
The European Union in International AffairsAs said before, WCE has rapidly become the standard minimally invasive method for visualization of the Small Bowel (SB) which is highly difficult to reach using classic endoscopy techniques like enteroscopy.作者: jumble 時(shí)間: 2025-3-25 08:08 作者: 沉積物 時(shí)間: 2025-3-25 13:13 作者: instate 時(shí)間: 2025-3-25 15:59 作者: 多嘴 時(shí)間: 2025-3-25 22:47 作者: hematuria 時(shí)間: 2025-3-26 02:56 作者: NIP 時(shí)間: 2025-3-26 07:47 作者: Encumber 時(shí)間: 2025-3-26 11:42 作者: 有組織 時(shí)間: 2025-3-26 14:01 作者: CRUC 時(shí)間: 2025-3-26 19:59 作者: 徹底檢查 時(shí)間: 2025-3-26 22:33
Jorge Bernal,Aymeric HistacePresents the first dedicated volume on computer-aided gastrointestinal video analysis.Provides insights from both technical and clinical domains, with a special focus on the clinical applicability of 作者: Monocle 時(shí)間: 2025-3-27 02:45
http://image.papertrans.cn/c/image/234417.jpg作者: declamation 時(shí)間: 2025-3-27 08:51
Clinical Context for Intelligent Systems in Colonoscopy.4 million new cases of CRC are diagnosed annually and the incidence rates are slightly higher in men than in women. The World Health Organization (WHO) reported a rate of mortality of . in 2018, as can be seen in Fig.?..作者: 離開(kāi)就切除 時(shí)間: 2025-3-27 10:10 作者: Herbivorous 時(shí)間: 2025-3-27 15:22 作者: refine 時(shí)間: 2025-3-27 19:20 作者: tinnitus 時(shí)間: 2025-3-27 23:07 作者: Heart-Attack 時(shí)間: 2025-3-28 02:40 作者: Countermand 時(shí)間: 2025-3-28 09:40 作者: CRP743 時(shí)間: 2025-3-28 13:28
Delphine Allès,Pascal Vennessongh transfer learning. However, in this work, we propose a deep learning architecture that exploits fine-tuning and random initialization of weights in a multi-encoder with a single decoder network architecture.作者: 贊成你 時(shí)間: 2025-3-28 15:36
https://doi.org/10.1007/978-3-030-91363-2r can be shared between classification, segmentation, and object detection network. Based on this observation, we wonder if it is possible to combine those different parts and solve multiple tasks in a single network to save the cost of training multiple networks.作者: Engulf 時(shí)間: 2025-3-28 19:08 作者: Resistance 時(shí)間: 2025-3-29 00:26
Clinical Context for Intelligent Systems in Colonoscopy.4 million new cases of CRC are diagnosed annually and the incidence rates are slightly higher in men than in women. The World Health Organization (WHO) reported a rate of mortality of . in 2018, as can be seen in Fig.?..作者: Rodent 時(shí)間: 2025-3-29 03:16 作者: 誘拐 時(shí)間: 2025-3-29 07:51 作者: Insufficient 時(shí)間: 2025-3-29 11:37 作者: PAD416 時(shí)間: 2025-3-29 18:48 作者: Microgram 時(shí)間: 2025-3-29 22:38
Multi-resolution Multi-task Network and Polyp Trackingr can be shared between classification, segmentation, and object detection network. Based on this observation, we wonder if it is possible to combine those different parts and solve multiple tasks in a single network to save the cost of training multiple networks.作者: Keshan-disease 時(shí)間: 2025-3-30 03:31 作者: 好忠告人 時(shí)間: 2025-3-30 04:25
ResNetANA challenge. In some cases like RTC-ATC group ResNet-50 was used as a layer in Faster Convolutional Neural Network (FCNN) in order to build an automated recognition system to detect the presence of polyps in colonoscopy images.作者: MIRE 時(shí)間: 2025-3-30 08:51 作者: multiply 時(shí)間: 2025-3-30 13:14
Convolutional LSTMysis (Wang et?al. .; Urban et?al. .; Shin et?al. .; Mohammed et?al. .). Colonoscopy, however, is a video-based modality and an endoscopist will always use the contextual information from previous frames to make an accurate decision about the potential presence of a polyp. Recent developments in sema作者: Toxoid-Vaccines 時(shí)間: 2025-3-30 18:12
Book 2021formance obtained by the 20 participating teams. The early and accurate diagnosis of gastrointestinal diseases is critical for increasing the chances of patient survival, and efficient screening is vital for locating precursor lesions. Video colonoscopy and wireless capsule endoscopy (WCE) are the g作者: cartilage 時(shí)間: 2025-3-30 21:07 作者: 轎車(chē) 時(shí)間: 2025-3-31 04:52
Multi-scale Ensemble of ResNet Variants to win many AI competitions. These methods are especially effective when the models are diverse?(Brown et?al. .). We achieve this diversity by using different ResNet models and by employing the multi-scale approach.作者: Adulate 時(shí)間: 2025-3-31 05:59
https://doi.org/10.1057/9781137443946ation of pixels, with a Markov Random Field property to improve the neighborhood of the lesion. This was done with the CIELab color space, since it was found that has high efficiency in differentiating colors in an image.作者: 反感 時(shí)間: 2025-3-31 10:12 作者: backdrop 時(shí)間: 2025-3-31 16:51
https://doi.org/10.1007/978-3-030-91363-2ence value. The anchors have different aspect ratios and scales. The classifier network crops these anchors from the feature maps of the last convolutional layer and feeds the cropped features to the remainder of the network in order to predict location and confidence values of the object class (polyps).作者: 逃避現(xiàn)實(shí) 時(shí)間: 2025-3-31 21:01
Combination of Color-Based Segmentation, Markov Random Fields and Multilayer Perceptronation of pixels, with a Markov Random Field property to improve the neighborhood of the lesion. This was done with the CIELab color space, since it was found that has high efficiency in differentiating colors in an image.
