標題: Titlebook: Applications of Medical Artificial Intelligence; Second International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2024 T [打印本頁] 作者: Gram114 時間: 2025-3-21 19:47
書目名稱Applications of Medical Artificial Intelligence影響因子(影響力)
書目名稱Applications of Medical Artificial Intelligence影響因子(影響力)學科排名
書目名稱Applications of Medical Artificial Intelligence網(wǎng)絡(luò)公開度
書目名稱Applications of Medical Artificial Intelligence網(wǎng)絡(luò)公開度學科排名
書目名稱Applications of Medical Artificial Intelligence被引頻次
書目名稱Applications of Medical Artificial Intelligence被引頻次學科排名
書目名稱Applications of Medical Artificial Intelligence年度引用
書目名稱Applications of Medical Artificial Intelligence年度引用學科排名
書目名稱Applications of Medical Artificial Intelligence讀者反饋
書目名稱Applications of Medical Artificial Intelligence讀者反饋學科排名
作者: babble 時間: 2025-3-22 00:19 作者: 方便 時間: 2025-3-22 04:02 作者: 延期 時間: 2025-3-22 05:46 作者: jabber 時間: 2025-3-22 12:37 作者: cognizant 時間: 2025-3-22 14:54
,Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and?Visualizics the staining processes, surpassing prior works and offering a feasible substitute for traditional histopathology methods. Preliminary results are presented using a clinical trial dataset pertaining to the CEACAM5 biomarker.作者: Aura231 時間: 2025-3-22 17:32 作者: excrete 時間: 2025-3-22 23:52
Investigating the Impact of Image Quality on Endoscopic AI Model Performance,, as well as on a manually selected dataset that includes images with lower subjective quality. Our findings highlight the importance of understanding the impact of a decrease in image quality and the need to include robustness evaluation for DNNs used in endoscopy.作者: Decibel 時間: 2025-3-23 04:03
Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection,cer detection in multi-modal MRI. Performance is evaluated on publicly-available databases, and compared to two state-of-the art baseline methods. The proposed ensembling approach improves the average precision metric in all considered applications, with a 8% gain for prostate cancer.作者: BUOY 時間: 2025-3-23 05:52 作者: palliate 時間: 2025-3-23 10:04 作者: Desert 時間: 2025-3-23 14:06 作者: BOOST 時間: 2025-3-23 21:36
Yoav Keynan MD, PhD,Ethan Rubinsteinion based on multi-view stereo. With Moving Least Squares and Poisson reconstruction we convert the point cloud into a mesh. This method is low-cost in hardware acquisition and supports minimal training time for a user to utilize it.作者: 莊嚴 時間: 2025-3-24 00:26 作者: Dedication 時間: 2025-3-24 02:58 作者: 構(gòu)成 時間: 2025-3-24 09:29
,Image-Based 3D Reconstruction of?Cleft Lip and?Palate Using a?Learned Shape Prior,ion based on multi-view stereo. With Moving Least Squares and Poisson reconstruction we convert the point cloud into a mesh. This method is low-cost in hardware acquisition and supports minimal training time for a user to utilize it.作者: 指耕作 時間: 2025-3-24 12:07 作者: 有毛就脫毛 時間: 2025-3-24 18:47 作者: Minuet 時間: 2025-3-24 21:24 作者: 射手座 時間: 2025-3-25 02:33 作者: Horizon 時間: 2025-3-25 03:42 作者: notification 時間: 2025-3-25 11:00 作者: constitutional 時間: 2025-3-25 14:46 作者: Curmudgeon 時間: 2025-3-25 15:51
,More Than Meets the?Eye: Physicians’ Visual Attention in?the?Operating Room,iologists’ visual attention (VA) is crucial. Moreover, the distribution of said VA and the acquisition of specific cues might directly impact patient outcomes..Recent research utilizes portable, head-mounted eye-tracking devices to gather precise and comprehensive information. Nevertheless, these te作者: antedate 時間: 2025-3-25 21:15
,CNNs vs. Transformers: Performance and?Robustness in?Endoscopic Image Analysis, which pose requirements on the performance, robustness and complexity of computer-based analysis techniques. This paper poses the question whether Transformer-based architectures, which are capable to directly capture global contextual information, can handle the aforementioned endoscopic condition作者: 火光在搖曳 時間: 2025-3-26 02:20
Investigating the Impact of Image Quality on Endoscopic AI Model Performance, heterogeneous. Endoscopic image quality can degrade by e.g. poor lighting, motion blur, and image compression. This disparity between training, validation data, and real-world clinical practice can have a substantial impact on the performance of deep neural networks (DNNs), potentially resulting in作者: 臨時抱佛腳 時間: 2025-3-26 07:53
Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection,pproach allows to benefit from both voxel-based and box-based predictions, thus improving the ability to accurately detect lesions. The method consists of 3 main steps: (i) semantic segmentation and object detection models are trained separately; (ii) voxel-based and box-based predictions are matche作者: 地名表 時間: 2025-3-26 10:08 作者: 悲觀 時間: 2025-3-26 15:32 作者: Keshan-disease 時間: 2025-3-26 18:25
,Video-Based Gait Analysis for?Assessing Alzheimer’s Disease and?Dementia with?Lewy Bodies,nificant role in clinical assessments to discriminate these neurological disorders from healthy controls, to grade disease severity, and to further differentiate dementia subtypes. In this paper, we propose a deep-learning based model specifically designed to evaluate gait impairment score for asses作者: 朦朧 時間: 2025-3-26 20:59
,Enhancing Clinical Support for?Breast Cancer with?Deep Learning Models Using Synthetic Correlated D As such, there has been immense research and progress on improving screening and clinical support for breast cancer. In this paper, we investigate enhancing clinical support for breast cancer with deep learning models using a newly introduced magnetic resonance imaging (MRI) modality called synthet作者: 密碼 時間: 2025-3-27 01:58 作者: CHYME 時間: 2025-3-27 08:37 作者: Offstage 時間: 2025-3-27 12:36 作者: EXTOL 時間: 2025-3-27 14:44
Ultrafast Labeling for Multiplexed Immunobiomarkers from Label-free Fluorescent Images, such as disease diagnostics and biomedical research. However, the existing predominant techniques harnessed for immunobiomarker labeling, such as immunofluorescence (IF) and immunohistochemistry (IHC), are marred by shortcomings such as inconsistent specificity, cost/time-intensive staining procedu作者: reject 時間: 2025-3-27 20:17
,M U-Net: Intestine Segmentation Using Multi-dimensional Features for?Ileus Diagnosis Assistance,ation to intestine obstruction diagnosis assistance called multi-dimensional U-Net (M U-Net). We employ two encoders to extract features from two-dimensional (2D) CT slices and three-dimensional (3D) CT patches. These two encoders collaborate to enhance the segmentation accuracy of the model. Additi作者: 容易做 時間: 2025-3-28 01:10
,Enhancing Cardiac MRI Segmentation via?Classifier-Guided Two-Stage Network and?All-Slice Informatio, and LV myocardium (MYO) in CMR images is crucial but time-consuming. Deep learning-based segmentation methods have emerged as effective tools for automating this process. However, CMR images present additional challenges due to irregular and varying heart shapes, particularly in basal and apical s作者: Facilities 時間: 2025-3-28 04:26
,Accessible Otitis Media Screening with?a?Deep Learning-Powered Mobile Otoscope,lack trained ear specialists, which prevents millions from being diagnosed and treated while causing severe complications. Currently, there is no viable diagnostic system for inexpensively and accurately detecting such ear conditions. This research presents OtoScan, a novel pipeline for the detectio作者: 辭職 時間: 2025-3-28 07:31 作者: 解脫 時間: 2025-3-28 13:50
Applications of Medical Artificial Intelligence978-3-031-47076-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: conquer 時間: 2025-3-28 15:54
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/159499.jpg作者: remission 時間: 2025-3-28 20:51
https://doi.org/10.1007/978-0-387-09437-3matoxylin-eosin (H &E) and immunohistochemistry (IHC) as relying solely on H &E can sometimes result in inaccurate cancer diagnoses. IHC examination offers additional evidence to support the diagnostic process. Given challenging accessibility issues of IHC examination, generating virtual IHC images 作者: Occlusion 時間: 2025-3-28 23:07 作者: Nonconformist 時間: 2025-3-29 06:25
Julien Matricon,Andrea Giuffrida PhD which pose requirements on the performance, robustness and complexity of computer-based analysis techniques. This paper poses the question whether Transformer-based architectures, which are capable to directly capture global contextual information, can handle the aforementioned endoscopic condition作者: Fretful 時間: 2025-3-29 11:03 作者: 純樸 時間: 2025-3-29 13:58 作者: Occupation 時間: 2025-3-29 16:13 作者: 相同 時間: 2025-3-29 22:20 作者: 六個才偏離 時間: 2025-3-30 02:05
https://doi.org/10.1007/978-3-319-57371-7nificant role in clinical assessments to discriminate these neurological disorders from healthy controls, to grade disease severity, and to further differentiate dementia subtypes. In this paper, we propose a deep-learning based model specifically designed to evaluate gait impairment score for asses作者: Processes 時間: 2025-3-30 05:03
https://doi.org/10.1007/978-3-319-57371-7 As such, there has been immense research and progress on improving screening and clinical support for breast cancer. In this paper, we investigate enhancing clinical support for breast cancer with deep learning models using a newly introduced magnetic resonance imaging (MRI) modality called synthet作者: 巨頭 時間: 2025-3-30 11:48
Yoav Keynan MD, PhD,Ethan Rubinsteinan be used to facilitate the plate treatment of the cleft and support surgery planning. A retrained LoFTR-based method creates an initial sparse point cloud. Next, we utilize our collection of existing scans of previous patients to train an implicit shape model. The shape model allows for refined de作者: 有危險 時間: 2025-3-30 12:40 作者: STYX 時間: 2025-3-30 19:25
https://doi.org/10.1007/978-3-319-27784-4ion and organization of these cells play a critical role in tumor progression. Single-cell analysis of histopathology images offers an intrinsic advantage over traditional patch-based approach by providing fine-grained cellular information. However, existing studies do not perform explicit cell clas作者: 木訥 時間: 2025-3-30 22:34 作者: concise 時間: 2025-3-31 03:06
Diagnostic Approach to Endocarditisation to intestine obstruction diagnosis assistance called multi-dimensional U-Net (M U-Net). We employ two encoders to extract features from two-dimensional (2D) CT slices and three-dimensional (3D) CT patches. These two encoders collaborate to enhance the segmentation accuracy of the model. Additi作者: COLON 時間: 2025-3-31 05:41 作者: allergy 時間: 2025-3-31 10:29 作者: 后來 時間: 2025-3-31 17:18
A. P. Weetman,R. S. McIntosh,P. F. Watsonpropriately placed stents or malappositions can result in post-interventional complications. Intravascular Ultrasound (IVUS) imaging offers a potential solution by providing real-time endovascular guidance for stent placement. The signature of malapposition is very subtle and requires exploring seco作者: ASTER 時間: 2025-3-31 19:49
https://doi.org/10.1007/978-3-031-47076-9artificial intelligence; machine learning; medical applications; medical imaging; deep learning; radiolog作者: 萬神殿 時間: 2025-4-1 01:43 作者: Felicitous 時間: 2025-4-1 05:15 作者: Constant 時間: 2025-4-1 06:45
