標(biāo)題: Titlebook: Computational Mathematics Modeling in Cancer Analysis; First International Wenjian Qin,Nazar Zaki,Fan Yang Conference proceedings 2022 The [打印本頁(yè)] 作者: 補(bǔ)給線 時(shí)間: 2025-3-21 17:58
書目名稱Computational Mathematics Modeling in Cancer Analysis影響因子(影響力)
書目名稱Computational Mathematics Modeling in Cancer Analysis影響因子(影響力)學(xué)科排名
書目名稱Computational Mathematics Modeling in Cancer Analysis網(wǎng)絡(luò)公開度
書目名稱Computational Mathematics Modeling in Cancer Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Mathematics Modeling in Cancer Analysis被引頻次
書目名稱Computational Mathematics Modeling in Cancer Analysis被引頻次學(xué)科排名
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書目名稱Computational Mathematics Modeling in Cancer Analysis年度引用學(xué)科排名
書目名稱Computational Mathematics Modeling in Cancer Analysis讀者反饋
書目名稱Computational Mathematics Modeling in Cancer Analysis讀者反饋學(xué)科排名
作者: 全神貫注于 時(shí)間: 2025-3-21 20:35 作者: 傾聽 時(shí)間: 2025-3-22 01:51 作者: 繼承人 時(shí)間: 2025-3-22 06:14 作者: 流浪者 時(shí)間: 2025-3-22 10:14 作者: capsaicin 時(shí)間: 2025-3-22 15:36 作者: capsaicin 時(shí)間: 2025-3-22 21:04
https://doi.org/10.1007/978-3-319-15446-6 at both low- and high-level feature learning stages are crucial in performance improvement. The proposed method outperforms state-of-the-art networks, achieving an average Dice of . at patch level, and an average accuracy of . at sample level, which is also verified in an independent cohort.作者: 壁畫 時(shí)間: 2025-3-23 00:00
,MLCN: Metric Learning Constrained Network for?Whole Slide Image Classification with?Bilinear Gated apture relations among sub-characteristics of tumor issues. Experiments on CAMELYON16 and TCGA Kidney datasets validate the effectiveness of our approach, and we achieved state-of-the-art performance compared to other popular methods. The codes will be available soon.作者: 紡織品 時(shí)間: 2025-3-23 05:02
,Cross-Stream Interactions: Segmentation of?Lung Adenocarcinoma Growth Patterns, at both low- and high-level feature learning stages are crucial in performance improvement. The proposed method outperforms state-of-the-art networks, achieving an average Dice of . at patch level, and an average accuracy of . at sample level, which is also verified in an independent cohort.作者: Afflict 時(shí)間: 2025-3-23 09:05 作者: Blood-Vessels 時(shí)間: 2025-3-23 10:59
0302-9743 onjunction with MICCAI 2022, in Singapore in September 2022. Due to the COVID-19 pandemic restrictions, the CMMCA2022 was held virtually...DALI 2022 accepted 15 papers from the 16 submissions that were reviewed. A major focus of CMMCA2022 is to identify new cutting-edge techniques and their applicat作者: BADGE 時(shí)間: 2025-3-23 17:10 作者: 真繁榮 時(shí)間: 2025-3-23 18:18
Computational Mathematics Modeling in Cancer AnalysisFirst International 作者: 意外的成功 時(shí)間: 2025-3-24 00:52 作者: CHOKE 時(shí)間: 2025-3-24 04:42
https://doi.org/10.1007/978-3-8348-8328-5lot cohort of 56 patients (total 95 WSIs). Interestingly, we observe that the tested machine learning models demonstrate a robust performance with just 1% randomly sampled patches from WSIs, on par with the model built on the entire WSI data. Among all three tested machine learning algorithms, multi作者: nerve-sparing 時(shí)間: 2025-3-24 09:13 作者: Insul島 時(shí)間: 2025-3-24 14:01 作者: 狂亂 時(shí)間: 2025-3-24 16:34
ROMK and Bartter Syndrome Type 2,ains an efficient feature encoder using a large amount of unlabeled spinal medical data with an image reconstruction task. Then this encoder is transferred to the downstream subtype classification in a multi-modal fusion model for fine-tuning. This multi-modal fusion model adopts a bipartite graph a作者: LUDE 時(shí)間: 2025-3-24 22:36 作者: 思考而得 時(shí)間: 2025-3-25 00:03 作者: itinerary 時(shí)間: 2025-3-25 05:08
https://doi.org/10.1007/978-981-97-1199-4mour features; and a Global Normalisation CAM module that combines local and global gradient information of tumour regions. Our VGG fusion and Global Normalisation CAM outperform the existing methods with a Dice of 84.188%. The final improvement for our proposed methods against the original rough la作者: 高興去去 時(shí)間: 2025-3-25 11:22
https://doi.org/10.1007/978-981-97-1199-4ith original image was input into the nnU-Net network for anatomical morphological information learning. Finally, we evaluated our proposed method on the clinical collection datasets with brachytherapy. Compared to the baseline model and state-of-the-art model, DSC and Recall were improved and the r作者: 食品室 時(shí)間: 2025-3-25 13:38
Competition, Decision, and Consensus,on. To validate and compare the transformer framework with various CNN-based methods, experiments have been conducted on the clinical dataset collection of NPC. The transformer framework outperformed the state-of-the-art pure CNN-based methods in AUC and recall. Especially, our framework achieved 2.作者: Hyperplasia 時(shí)間: 2025-3-25 17:33 作者: AMBI 時(shí)間: 2025-3-25 21:46
,Cellular Architecture on?Whole Slide Images Allows the?Prediction of?Survival in?Lung Adenocarcinome demonstrated that by pruning redundant and irrelevant features, the final prediction model has achieved an optimal C-index of 0.70 during testing. Our proof-of-concept study proves that the efficient local-global embedded maps bear valuable information with clinical correlations in lung cancer and作者: 口訣法 時(shí)間: 2025-3-26 00:16 作者: 模仿 時(shí)間: 2025-3-26 05:55
,Repeatability of?Radiomic Features Against Simulated Scanning Position Stochasticity Across Imagingments across imaging modalities and HNC subtypes. Bias from feature collinearity was also investigated. All the shape RFs and the majority of RFs from unfiltered (.83.5%) and LoG-filtered (.93%) images showed high repeatability (ICC . 0.9) in all studied datasets, whereas more than 50% of the wavele作者: 多產(chǎn)子 時(shí)間: 2025-3-26 10:22
,NucDETR: End-to-End Transformer for?Nucleus Detection in?Histopathology Images,monstrating its effectiveness and benchmarking the performance of Transformer detectors on histopathology images. Where applicable, we also propose remedies that mitigate some of the issues faced when adopting such Transformer-based detection. The proposed end-to-end architecture avoids much of the 作者: dearth 時(shí)間: 2025-3-26 16:23 作者: 故意釣到白楊 時(shí)間: 2025-3-26 19:17
,Clustering-Based Multi-instance Learning Network for?Whole Slide Image Classification,odel, and the weights of the WSI patches are calculated by their similarity to the phenotypic centroids to highlight the significant patches. Our method is evaluated on two public WSI datasets (CAMELYON16 and TCGA-Lung) for binary tumor and cancer sub-types classification and achieves better perform作者: NAG 時(shí)間: 2025-3-26 21:35 作者: analogous 時(shí)間: 2025-3-27 03:41 作者: 露天歷史劇 時(shí)間: 2025-3-27 08:14 作者: 兒童 時(shí)間: 2025-3-27 09:48
Automatic Computer-Aided Histopathologic Segmentation for Nasopharyngeal Carcinoma Using Transformeon. To validate and compare the transformer framework with various CNN-based methods, experiments have been conducted on the clinical dataset collection of NPC. The transformer framework outperformed the state-of-the-art pure CNN-based methods in AUC and recall. Especially, our framework achieved 2.作者: Crumple 時(shí)間: 2025-3-27 14:06
Accurate Breast Tumor Identification Using Computational Ultrasound Image Features, proposed algorithm achieved a diagnostic accuracy of 89.32% and a significant area under curve (AUC) of 0.9473 with the repeated cross-validation scheme. In conclusion, our algorithm shows superior performance over the existing classical methods and can be potentially applied to breast cancer scree作者: 冒煙 時(shí)間: 2025-3-27 20:04 作者: 防止 時(shí)間: 2025-3-27 22:07
,Is More Always Better? Effects of?Patch Sampling in?Distinguishing Chronic Lymphocytic Leukemia froransformation; RT) has important clinical implications that greatly influence patient management. However, distinguishing between these disease phases on histologic grounds may be challenging in routine practice due to the presence of similar structures and homogeneous intensity, among others. In th作者: Flirtatious 時(shí)間: 2025-3-28 03:08 作者: colony 時(shí)間: 2025-3-28 06:59
,MLCN: Metric Learning Constrained Network for?Whole Slide Image Classification with?Bilinear Gated eved good results, the classification performance is still unsatisfactory because the learned features of WSI lack discrimination and the correlation among sub-characteristics of tumor images are ignored. In this paper, we proposed a Metric Learning Constraint Network (referred to as MLCN). Particul作者: resuscitation 時(shí)間: 2025-3-28 13:47
,NucDETR: End-to-End Transformer for?Nucleus Detection in?Histopathology Images,pensive task if done manually by experienced clinicians, and is also prone to subjectivity and inconsistency. Alternatively, the advancement in computer vision-based analysis enables the automatic detection of cancerous nuclei; however, the task poses several challenges due to the heterogeneity in t作者: 來就得意 時(shí)間: 2025-3-28 17:16
Self-supervised Learning Based on a Pre-trained Method for the Subtype Classification of Spinal Tum tumor subtypes from medical images in the early stage is of great clinical significance. Due to the complex morphology and high heterogeneity of spinal tumors, it can be challenging to diagnose subtypes from medical images accurately. In recent years, a number of researchers have applied deep learn作者: 婚姻生活 時(shí)間: 2025-3-28 21:59
,CanDLE: Illuminating Biases in?Transcriptomic Pan-Cancer Diagnosis, task could be a valuable support in clinical practice and provide insights into the cancer causal mechanisms. To correctly approach this problem, the largest existing resource (The Cancer Genome Atlas) must be complemented with healthy tissue samples from the Genotype-Tissue Expression project. In 作者: Anguish 時(shí)間: 2025-3-29 00:15
,Cross-Stream Interactions: Segmentation of?Lung Adenocarcinoma Growth Patterns,terns in routine histology samples is challenging due to the complexity of patterns and high intra-class variability. In this paper, we present a novel model with a multi-stream architecture, Cross-Stream Interactions (CroSIn), which fully considers crucial interactions across scales to gather abund作者: parasite 時(shí)間: 2025-3-29 03:47 作者: 他去就結(jié)束 時(shí)間: 2025-3-29 10:13 作者: GOAT 時(shí)間: 2025-3-29 11:50 作者: OFF 時(shí)間: 2025-3-29 19:11
,Light Annotation Fine Segmentation: Histology Image Segmentation Based on?VGG Fusion with?Global Noonsuming. To reduce the manual annotation workload, we propose a light annotation-based fine-level segmentation approach for histology images based on a VGG-based Fusion network with Global Normalisation CAM. The experts are only required to provide a rough segmentation annotation on the images, and作者: Processes 時(shí)間: 2025-3-29 20:49
,Tubular Structure-Aware Convolutional Neural Networks for?Organ at?Risks Segmentation in?Cervical C are called Organ at Risks (OARs), which are prone to irreversible damage during radiotherapy. Therefore, accurate delineation of OARs is a critical step in ensuring radiotherapy dosimetry accuracy. However, currently existing deep learning-based cervical cancer OARs segmentation methods do not make作者: 笨拙處理 時(shí)間: 2025-3-30 01:37 作者: 易受刺激 時(shí)間: 2025-3-30 06:29
Accurate Breast Tumor Identification Using Computational Ultrasound Image Features,. Ultrasound plays a key role and yet provides an economical solution for breast cancer screening. While valuable, ultrasound is still suffered from limited specificity, and its accuracy is highly related to the clinicians, resulting in inconsistent diagnosis. To address the challenge of limited spe作者: COUCH 時(shí)間: 2025-3-30 09:23 作者: 嚙齒動(dòng)物 時(shí)間: 2025-3-30 14:20 作者: insightful 時(shí)間: 2025-3-30 18:40 作者: 鈍劍 時(shí)間: 2025-3-30 21:23
,“The Eye Altering Alters All”,eved good results, the classification performance is still unsatisfactory because the learned features of WSI lack discrimination and the correlation among sub-characteristics of tumor images are ignored. In this paper, we proposed a Metric Learning Constraint Network (referred to as MLCN). Particul作者: 緊張過度 時(shí)間: 2025-3-31 03:07
Donald D. F. Loo,Ernest M. Wrightpensive task if done manually by experienced clinicians, and is also prone to subjectivity and inconsistency. Alternatively, the advancement in computer vision-based analysis enables the automatic detection of cancerous nuclei; however, the task poses several challenges due to the heterogeneity in t作者: Amendment 時(shí)間: 2025-3-31 05:06
ROMK and Bartter Syndrome Type 2, tumor subtypes from medical images in the early stage is of great clinical significance. Due to the complex morphology and high heterogeneity of spinal tumors, it can be challenging to diagnose subtypes from medical images accurately. In recent years, a number of researchers have applied deep learn作者: 期滿 時(shí)間: 2025-3-31 12:49
Beata Plesiewicz,Jan Wiszniowski task could be a valuable support in clinical practice and provide insights into the cancer causal mechanisms. To correctly approach this problem, the largest existing resource (The Cancer Genome Atlas) must be complemented with healthy tissue samples from the Genotype-Tissue Expression project. In
