標(biāo)題: Titlebook: Cancer Prevention Through Early Detection; First International Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceedings 202 [打印本頁] 作者: 戲弄 時間: 2025-3-21 16:23
書目名稱Cancer Prevention Through Early Detection影響因子(影響力)
書目名稱Cancer Prevention Through Early Detection影響因子(影響力)學(xué)科排名
書目名稱Cancer Prevention Through Early Detection網(wǎng)絡(luò)公開度
書目名稱Cancer Prevention Through Early Detection網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Cancer Prevention Through Early Detection被引頻次
書目名稱Cancer Prevention Through Early Detection被引頻次學(xué)科排名
書目名稱Cancer Prevention Through Early Detection年度引用
書目名稱Cancer Prevention Through Early Detection年度引用學(xué)科排名
書目名稱Cancer Prevention Through Early Detection讀者反饋
書目名稱Cancer Prevention Through Early Detection讀者反饋學(xué)科排名
作者: FLASK 時間: 2025-3-21 21:27
Cancer Prevention Through Early Detection978-3-031-17979-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Loathe 時間: 2025-3-22 02:55
https://doi.org/10.1007/978-3-031-17979-2artificial intelligence; bioinformatics; classification methods; computer systems; computer vision; deep 作者: Intellectual 時間: 2025-3-22 05:34 作者: 膽小懦夫 時間: 2025-3-22 11:49
FDE as a Base for Constructive Logic The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ’s surface is developed, and coupled with an automatic extraction of morphologi作者: 牙齒 時間: 2025-3-22 16:01 作者: 牙齒 時間: 2025-3-22 17:21 作者: 昆蟲 時間: 2025-3-22 21:59
Frege on Dichtung and Elucidation,t such a pipeline, it is often necessary to train a classifier, which decides if a patch (for example, sized .) provides a positive cell reading or not. Following the clinical guidance, pathologists must label many such patches of both negative (N) and positive (P) cells. Then, a deep network can be作者: 舊病復(fù)發(fā) 時間: 2025-3-23 02:53 作者: 狂怒 時間: 2025-3-23 09:23 作者: abolish 時間: 2025-3-23 11:37
,Railroading the Novel: Gayl Jones’s ,ion is providing crucial diagnostic support, however, subtle lesions in upper and lower GI are quite hard to detect and cause considerable missed detection. In this work, we leverage deep learning to develop a framework to improve the localization of difficult to detect lesions and minimize the miss作者: 抱怨 時間: 2025-3-23 16:26
,Railroading the Novel: Gayl Jones’s ,perience. To address the challenge of automated lesion risk assessment, based on Wireless Capsule Endoscopy (WCE) images, this paper introduces a novel Artificial Intelligence (AI) framework based on Fuzzy Cognitive Maps (FCMs). Specifically, FCMs are fuzzy graph structures used to model knowledge s作者: modifier 時間: 2025-3-23 19:01 作者: 旅行路線 時間: 2025-3-23 22:26 作者: Banister 時間: 2025-3-24 03:01 作者: 半球 時間: 2025-3-24 08:26
Popular Culture, Ethnicities and Tastes,radiomics features, we propose to learn a new set of features from histology. Generating a comprehensive lung histology report is the first vital step toward this goal. Deep learning has revolutionised the computational assessment of digital pathology images. Today, we have mature algorithms for ass作者: Insubordinate 時間: 2025-3-24 13:58 作者: 妨礙議事 時間: 2025-3-24 18:29 作者: IRATE 時間: 2025-3-24 21:53 作者: 原來 時間: 2025-3-25 00:03
Conference proceedings 2022it their work in the field of medical imaging around the central theme of early cancer detection, and it strives to address the challenges that are required to be overcomed to translate computational methods to clinical practice through well designed, generalizable (robust), interpretable and clinically transferable methods..作者: Inertia 時間: 2025-3-25 03:23 作者: faculty 時間: 2025-3-25 08:58
0302-9743 hat are required to be overcomed to translate computational methods to clinical practice through well designed, generalizable (robust), interpretable and clinically transferable methods..978-3-031-17978-5978-3-031-17979-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 擴(kuò)張 時間: 2025-3-25 14:51 作者: Comprise 時間: 2025-3-25 17:45 作者: 小教堂 時間: 2025-3-25 22:21 作者: nominal 時間: 2025-3-26 04:04 作者: 安裝 時間: 2025-3-26 04:31 作者: 惡意 時間: 2025-3-26 11:20
Knowledge Distillation with?a?Class-Aware Loss for?Endoscopic Disease Detectiondoscopic disease detection (EDD2020) challenge and Kvasir-SEG datasets. Additionally, we show that using such learning paradigm, our model is generalizable to unseen test set giving higher APs for clinically crucial neoplastic and polyp categories.作者: 工作 時間: 2025-3-26 15:12 作者: RAFF 時間: 2025-3-26 17:09
FDE as a Base for Constructive Logic Three other sets of classical features are trained and tested, (1) clinical relevant features gives an AUC of 0.58, (2) 111 radiomics gives an AUC of 0.976, (3) radiologist ground truth (GT) containing the nodule size, attenuation and spiculation qualitative annotations gives an AUC of 0.979. We al作者: CLEFT 時間: 2025-3-26 23:57
https://doi.org/10.1007/978-3-030-31136-0k of solving a jigsaw puzzle. Here, the idea is to enable network to distinctly learn inconspicuous features that are characteristics of neoplasia and other classes. In order to enable an optimal decision boundary we propose to incorporated angular margin in our fine-tuning process. Our proposed fra作者: FLIC 時間: 2025-3-27 01:39
Allen P. Hazen,Francis Jeffry Pelletier IM gastric glands. To evaluate the efficiency of the proposed methodology we created the IMGL dataset consisting of 1000 gland images, including both intestinal metaplasia and normal cases received from 20 Whole Slide Images (WSI). The results showed that the proposed approach achieves an F1 score 作者: Adjourn 時間: 2025-3-27 05:33
Frege on Dichtung and Elucidation,also align the EN-HN and HN-P decision planes in parallel in the latent feature space where all input patches are encoded. The dual planes perform parallel classification then, following a well-planned curriculum learning scheme. Our results show that the proposed method can greatly enhance the perf作者: 男學(xué)院 時間: 2025-3-27 13:11
,Railroading the Novel: Gayl Jones’s ,tegrated into any classifier. To demonstrate its performance, experiments were conducted using real datasets, which include a variety of GI abnormalities, and different feature extractors. The results show that the automatically constructed FCM outperforms state-of-the-art methods, while providing i作者: archetype 時間: 2025-3-27 16:36
Anscombe on Expression of Intention the post-hoc methods on average by . and . for accuracy and specificity, respectively. The obtained results show promising methods towards real-time endoscopic video analysis and identifies steps for further development.作者: 預(yù)測 時間: 2025-3-27 21:22 作者: Boycott 時間: 2025-3-28 01:44
Popular Culture, Ethnicities and Tastes,RPs during the colonoscopy procedure is essential for an appropriate treatment strategy. In this paper, we incorporate Bayesian variational inference and investigate the performance of a hybrid Bayesian neural network-based CADx system for the characterization of CRPs. Results of conducted experimen作者: maverick 時間: 2025-3-28 03:56 作者: 不能仁慈 時間: 2025-3-28 07:05 作者: 使入迷 時間: 2025-3-28 14:22 作者: 不連貫 時間: 2025-3-28 16:13
Robert Gordon,Marizanne Grundlingho be performed virtually for individual patients, based on their anatomy and lesions derived from MR images. A patient-specific policy can thus be optimised, before each biopsy procedure, by rewarding positive sampling in the MDP environment. Experiment results from fifty four prostate cancer patien作者: cuticle 時間: 2025-3-28 20:01
3D-Morphomics, Morphological Features on?CT Scans for?Lung Nodule Malignancy Diagnosis Three other sets of classical features are trained and tested, (1) clinical relevant features gives an AUC of 0.58, (2) 111 radiomics gives an AUC of 0.976, (3) radiologist ground truth (GT) containing the nodule size, attenuation and spiculation qualitative annotations gives an AUC of 0.979. We al作者: motor-unit 時間: 2025-3-29 00:58
Self-supervised Approach for?a?Fully Assistive Esophageal Surveillance: Quality, Anatomy and?Neoplask of solving a jigsaw puzzle. Here, the idea is to enable network to distinctly learn inconspicuous features that are characteristics of neoplasia and other classes. In order to enable an optimal decision boundary we propose to incorporated angular margin in our fine-tuning process. Our proposed fra作者: 變化無常 時間: 2025-3-29 05:02
Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset IM gastric glands. To evaluate the efficiency of the proposed methodology we created the IMGL dataset consisting of 1000 gland images, including both intestinal metaplasia and normal cases received from 20 Whole Slide Images (WSI). The results showed that the proposed approach achieves an F1 score 作者: Alopecia-Areata 時間: 2025-3-29 07:31
Parallel Classification of?Cells in?Thinprep Cytology Test Image for?Cervical Cancer Screeningalso align the EN-HN and HN-P decision planes in parallel in the latent feature space where all input patches are encoded. The dual planes perform parallel classification then, following a well-planned curriculum learning scheme. Our results show that the proposed method can greatly enhance the perf作者: CHANT 時間: 2025-3-29 13:03 作者: Arthropathy 時間: 2025-3-29 18:33
A CAD System for?Real-Time Characterization of?Neoplasia in?Barrett’s Esophagus NBI Videos the post-hoc methods on average by . and . for accuracy and specificity, respectively. The obtained results show promising methods towards real-time endoscopic video analysis and identifies steps for further development.作者: 內(nèi)行 時間: 2025-3-29 21:07 作者: 漸強(qiáng) 時間: 2025-3-30 01:47 作者: 和平主義者 時間: 2025-3-30 07:32
Active Data Enrichment by?Learning What to?Annotate in?Digital Pathologyse and compare approaches aimed to balance the dataset and mitigate the biases in learning by automatically prioritising regions with clinical patterns underrepresented in the dataset. Our study demonstrates the opportunities active data enrichment can provide and results in a new lung-cancer datase作者: TEM 時間: 2025-3-30 11:36
Comparing Training Strategies Using Multi-Assessor Segmentation Labels for?Barrett’s Neoplasia Detecection. The value used to generate this curve is the maximum pixel value in the raw segmentation map, and the histologically proven ground truth of the image. The experiments show that random sampling of the four neoplastic areas together with a compound loss Binary Cross-entropy and DICE yields the作者: 舔食 時間: 2025-3-30 15:47
Improved Pancreatic Tumor Detection by?Utilizing Clinically-Relevant Secondary Features based on a U-Net-like Deep CNN that exploits the following external secondary features: the pancreatic duct, common bile duct and the pancreas, along with a processed CT scan. Using these features, the model segments the pancreatic tumor if it is present. This segmentation for classification and lo作者: GUILE 時間: 2025-3-30 17:31 作者: 精致 時間: 2025-3-30 23:13
3D-Morphomics, Morphological Features on?CT Scans for?Lung Nodule Malignancy Diagnosis The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ’s surface is developed, and coupled with an automatic extraction of morphologi作者: Cloudburst 時間: 2025-3-31 03:55
Self-supervised Approach for?a?Fully Assistive Esophageal Surveillance: Quality, Anatomy and?Neoplasgnosis and treatment. While endoscopic videos are corrupted with multiple artefacts and procedure require investigating extended areas such as stomach, it is inevitable that there is risk of missing areas that may potentially harbour neoplastic changes and require immediate attention. A complete gui作者: habile 時間: 2025-3-31 07:58 作者: CLAN 時間: 2025-3-31 10:40 作者: 解凍 時間: 2025-3-31 15:59
Lightweight Transformer Backbone for?Medical Object Detectiong tumors. Due to the label scarcity problem, large deep learning models and computationally intensive algorithms are likely to fail when applied to this task. In this paper, we present a practical yet lightweight backbone to improve the accuracy of tumor detection. Specifically, we propose a novel m作者: 代理人 時間: 2025-3-31 20:11
Contrastive and?Attention-Based Multiple Instance Learning for?the?Prediction of?Sentinel Lymph Nodet at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited da作者: V洗浴 時間: 2025-3-31 23:57
Knowledge Distillation with?a?Class-Aware Loss for?Endoscopic Disease Detectionion is providing crucial diagnostic support, however, subtle lesions in upper and lower GI are quite hard to detect and cause considerable missed detection. In this work, we leverage deep learning to develop a framework to improve the localization of difficult to detect lesions and minimize the miss
