標(biāo)題: Titlebook: Machine Learning in Medical Imaging; 14th International W Xiaohuan Cao,Xuanang Xu,Xi Ouyang Conference proceedings 2024 The Editor(s) (if a [打印本頁] 作者: 次要 時(shí)間: 2025-3-21 16:59
書目名稱Machine Learning in Medical Imaging影響因子(影響力)
書目名稱Machine Learning in Medical Imaging影響因子(影響力)學(xué)科排名
書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度
書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning in Medical Imaging被引頻次
書目名稱Machine Learning in Medical Imaging被引頻次學(xué)科排名
書目名稱Machine Learning in Medical Imaging年度引用
書目名稱Machine Learning in Medical Imaging年度引用學(xué)科排名
書目名稱Machine Learning in Medical Imaging讀者反饋
書目名稱Machine Learning in Medical Imaging讀者反饋學(xué)科排名
作者: 愛了嗎 時(shí)間: 2025-3-21 23:46 作者: hypertension 時(shí)間: 2025-3-22 02:41
978-3-031-45675-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 收到 時(shí)間: 2025-3-22 05:40 作者: Embolic-Stroke 時(shí)間: 2025-3-22 09:59 作者: Outspoken 時(shí)間: 2025-3-22 14:19 作者: 西瓜 時(shí)間: 2025-3-22 19:47 作者: 方舟 時(shí)間: 2025-3-22 21:28
,Identifying Alzheimer’s Disease-Induced Topology Alterations in Structural Networks Using Convoluti (AD). However, conventional graph learning methods struggle to accurately represent the subtle and heterogeneous topology alterations caused by AD, leading to marginal classification accuracy. In this study, we address this issue through a two-fold approach. Firstly, to more reliably capture AD-ind作者: 和平 時(shí)間: 2025-3-23 04:58
,Specificity-Aware Federated Graph Learning for?Brain Disorder Analysis with?Functional MRI,by brain disorders. Graph neural network (GNN) has been widely used for fMRI representation learning and brain disorder analysis, thanks to its potent graph representation abilities. Training a generalizable GNN model often requires large-scale subjects from different medical centers/sites, but the 作者: 驚奇 時(shí)間: 2025-3-23 08:45 作者: linear 時(shí)間: 2025-3-23 16:54
,Cross-view Contrastive Mutual Learning Across Masked Autoencoders for?Mammography Diagnosis,is study, we propose a novel cross-view mutual learning method that leverages a Cross-view Masked Autoencoder (CMAE) and a Dual-View Affinity Matrix (DAM) to extract cross-view features and facilitate malignancy classification in mammography. CMAE aims to extract the underlying features from multi-v作者: 少量 時(shí)間: 2025-3-23 21:49 作者: outrage 時(shí)間: 2025-3-23 23:16
,Boundary-Constrained Graph Network for?Tooth Segmentation on?3D Dental Surfaces, have been proposed for automatic tooth segmentation. However, previous tooth segmentation methods often face challenges in accurately delineating boundaries, leading to a decline in overall segmentation performance. In this paper, we propose a boundary-constrained graph-based neural network that es作者: 合群 時(shí)間: 2025-3-24 04:56
,FAST-Net: A Coarse-to-fine Pyramid Network for?Face-Skull Transformation,uch as forensic facial reconstruction and craniomaxillofacial (CMF) surgery planning. However, this transformation is a challenging task due to the significant differences between the geometric topologies of the face and skull shapes. In this paper, we propose a novel coarse-to-fine face-skull trans作者: Flu表流動(dòng) 時(shí)間: 2025-3-24 09:17
,Mixing Histopathology Prototypes into?Robust Slide-Level Representations for?Cancer Subtyping,ls available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an under-explored alternative model to common vision transformers, especially for large-scale dat作者: 高深莫測 時(shí)間: 2025-3-24 14:26
,Consistency Loss for?Improved Colonoscopy Landmark Detection with?Vision Transformers,om the actual diagnosis, manually processing the snapshots taken during the colonoscopy procedure (for medical record keeping) consumes a large amount of the clinician’s time. This can be automated through post-procedural machine learning based algorithms which classify anatomical landmarks in the c作者: 無所不知 時(shí)間: 2025-3-24 16:31 作者: characteristic 時(shí)間: 2025-3-24 22:34 作者: Ablation 時(shí)間: 2025-3-25 00:57
,Enhancing Anomaly Detection in?Melanoma Diagnosis Through Self-Supervised Training and?Lesion Comparements. While considerable research has addressed melanoma diagnosis using convolutional neural networks (CNNs) on individual dermatological images, a deeper exploration of lesion comparison within a patient is warranted for enhanced anomaly detection, which often signifies malignancy. In this stud作者: gospel 時(shí)間: 2025-3-25 06:59 作者: 拖債 時(shí)間: 2025-3-25 10:04
,Precise Localization Within the?GI Tract by?Combining Classification of?CNNs and?Time-Series Analysloring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM). It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. Our approach achieves an accuracy of . 作者: figure 時(shí)間: 2025-3-25 12:12
,Towards Unified Modality Understanding for?Alzheimer’s Disease Diagnosis Using Incomplete Multi-modnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, incomplete multi-modality data often limits the diagnostic performance of deep learning-based methods, as only partial data can be used for training neural networks, and meanwhile it is challen作者: 細(xì)節(jié) 時(shí)間: 2025-3-25 15:54 作者: flex336 時(shí)間: 2025-3-25 23:45 作者: WATER 時(shí)間: 2025-3-26 02:49
Yu Tian,Guansong Pang,Yuyuan Liu,Chong Wang,Yuanhong Chen,Fengbei Liu,Rajvinder Singh,Johan W. Verja life.Asks whether personal immortality is possible.What are life and death? Is it possible to understand their essence and give clear definitions? Countless books and articles have been devoted to trying to answer these intriguing questions. However, there are still no definite and generally accept作者: 鉗子 時(shí)間: 2025-3-26 05:01 作者: 使出神 時(shí)間: 2025-3-26 12:27
Feihong Liu,Yongsheng Pan,Junwei Yang,Fang Xie,Xiaowei He,Han Zhang,Feng Shi,Jun Feng,Qihao Guo,Ding life.Asks whether personal immortality is possible.What are life and death? Is it possible to understand their essence and give clear definitions? Countless books and articles have been devoted to trying to answer these intriguing questions. However, there are still no definite and generally accept作者: Nerve-Block 時(shí)間: 2025-3-26 15:22
Junhao Zhang,Xiaochuan Wang,Qianqian Wang,Lishan Qiao,Mingxia Liu life.Asks whether personal immortality is possible.What are life and death? Is it possible to understand their essence and give clear definitions? Countless books and articles have been devoted to trying to answer these intriguing questions. However, there are still no definite and generally accept作者: 運(yùn)動(dòng)性 時(shí)間: 2025-3-26 19:40
Huy-Dung Nguyen,Micha?l Clément,Boris Mansencal,Pierrick Coupépublication of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself 作者: Host142 時(shí)間: 2025-3-26 23:56 作者: Throttle 時(shí)間: 2025-3-27 01:46
Qingxia Wu,Hongna Tan,Zhi Qiao,Pei Dong,Dinggang Shen,Meiyun Wang,Zhong Xuepublication of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself 作者: 百靈鳥 時(shí)間: 2025-3-27 07:19
Nan Zhao,Yongsheng Pan,Kaicong Sun,Yuning Gu,Mianxin Liu,Zhong Xue,Han Zhang,Qing Yang,Fei Gao,Feng publication of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself 作者: 騙子 時(shí)間: 2025-3-27 11:12
Yuwen Tan,Xiang Xiangn of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself are not an作者: oblique 時(shí)間: 2025-3-27 15:27
Lei Zhao,Lei Ma,Zhiming Cui,Jie Zheng,Zhong Xue,Feng Shi,Dinggang Shenn of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself are not an作者: FECT 時(shí)間: 2025-3-27 18:31
Joshua Butke,Noriaki Hashimoto,Ichiro Takeuchi,Hiroaki Miyoshi,Koichi Ohshima,Jun Sakuman of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself are not an作者: Jacket 時(shí)間: 2025-3-28 00:00 作者: crockery 時(shí)間: 2025-3-28 05:19
Lanhong Yao,Zheyuan Zhang,Ugur Demir,Elif Keles,Camila Vendrami,Emil Agarunov,Candice Bolan,Ivo Schon of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself are not an作者: HARP 時(shí)間: 2025-3-28 06:55
Boning Tong,Zhuoping Zhou,Davoud Ataee Tarzanagh,Bojian Hou,Andrew J. Saykin,Jason Moore,Marylyn Ritpublication of What Is Life? by Schrodinger ¨ and after the rise and success of molecular biology, have we reached the answer to these questions? In the recent years, I have often been asked by young researchers and students in biology: “I am afraid that such basic questions on a life system itself 作者: 印第安人 時(shí)間: 2025-3-28 12:56 作者: cutlery 時(shí)間: 2025-3-28 15:15 作者: 萬靈丹 時(shí)間: 2025-3-28 19:14 作者: BURSA 時(shí)間: 2025-3-29 00:35 作者: SUGAR 時(shí)間: 2025-3-29 04:10 作者: 消毒 時(shí)間: 2025-3-29 08:21 作者: Peristalsis 時(shí)間: 2025-3-29 12:55 作者: 共同生活 時(shí)間: 2025-3-29 17:35
Conference proceedings 2024semble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc..作者: Grating 時(shí)間: 2025-3-29 23:47
,Consisaug: A Consistency-Based Augmentation for?Polyp Detection in?Endoscopy Image Analysis,eep learning. We utilize the constraint that when the image is flipped the class label should be equal and the bonding boxes should be consistent. We implement our Consisaug on five public polyp datasets and at three backbones, and the results show the effectiveness of our method. All the codes are available at (.).作者: Vulvodynia 時(shí)間: 2025-3-30 03:23
Yuwen Tan,Xiang Xiangboth theoretically and experimentally, in lectures and seminars. Although they show much interest, introduction of these rather interdisciplinary style of research is not easy, let alone discussing how we can understand life. Of course they ask for some books that describe a theoretical basis of our作者: 坦白 時(shí)間: 2025-3-30 04:34
Lei Zhao,Lei Ma,Zhiming Cui,Jie Zheng,Zhong Xue,Feng Shi,Dinggang Shenboth theoretically and experimentally, in lectures and seminars. Although they show much interest, introduction of these rather interdisciplinary style of research is not easy, let alone discussing how we can understand life. Of course they ask for some books that describe a theoretical basis of our作者: Conclave 時(shí)間: 2025-3-30 10:55
Joshua Butke,Noriaki Hashimoto,Ichiro Takeuchi,Hiroaki Miyoshi,Koichi Ohshima,Jun Sakumaboth theoretically and experimentally, in lectures and seminars. Although they show much interest, introduction of these rather interdisciplinary style of research is not easy, let alone discussing how we can understand life. Of course they ask for some books that describe a theoretical basis of our作者: 內(nèi)向者 時(shí)間: 2025-3-30 14:59
Lanhong Yao,Zheyuan Zhang,Ugur Demir,Elif Keles,Camila Vendrami,Emil Agarunov,Candice Bolan,Ivo Schoboth theoretically and experimentally, in lectures and seminars. Although they show much interest, introduction of these rather interdisciplinary style of research is not easy, let alone discussing how we can understand life. Of course they ask for some books that describe a theoretical basis of our作者: 生氣的邊緣 時(shí)間: 2025-3-30 18:32 作者: Cacophonous 時(shí)間: 2025-3-31 00:29 作者: 英寸 時(shí)間: 2025-3-31 03:49
,GEMTrans: A General, Echocardiography-Based, Multi-level Transformer Framework for?Cardiovascular D. To remedy this, we propose a .eneral, .cho-based, .ulti-Level .ransformer (GEMTrans) framework that provides explainability, while simultaneously enabling multi-video training where the inter-play among echo image patches in the same frame, all frames in the same video, and inter-video relationshi作者: 大喘氣 時(shí)間: 2025-3-31 05:51
,Unsupervised Anomaly Detection in?Medical Images with?a?Memory-Augmented Multi-level Cross-Attentio(MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder. MemMC-MAE masks large parts of the input image during its reconstruction, reducing the risk that it will produc作者: 可用 時(shí)間: 2025-3-31 12:27
,LMT: Longitudinal Mixing Training, a?Framework to?Predict Disease Progression from?a?Single Image,ongitudinal Mixing Training (LMT), can be considered both as a regularizer and as a pretext task that encodes the disease progression in the latent space. Additionally, we evaluate the trained model weights on a downstream task with a longitudinal context using standard and longitudinal pretext task作者: nephritis 時(shí)間: 2025-3-31 17:14 作者: Perigee 時(shí)間: 2025-3-31 18:27 作者: Hangar 時(shí)間: 2025-4-1 00:48
,3D Transformer Based on?Deformable Patch Location for?Differential Diagnosis Between Alzheimer’s Dimentation techniques, adapted for training transformer-based models on 3D structural magnetic resonance imaging data. Finally, we propose to combine our transformer-based model with a traditional machine learning model using brain structure volumes to better exploit the available data. Our experimen作者: Obstreperous 時(shí)間: 2025-4-1 04:24 作者: 適宜 時(shí)間: 2025-4-1 08:52 作者: 致詞 時(shí)間: 2025-4-1 13:25
,Boundary-Constrained Graph Network for?Tooth Segmentation on?3D Dental Surfaces,boundary mesh cells. Following the network prediction, we apply a post-processing step based on the graph cut to refine the boundaries. Experimental results demonstrate that our method achieves state-of-the-art performance in 3D tooth segmentation.作者: exophthalmos 時(shí)間: 2025-4-1 14:54 作者: Asymptomatic 時(shí)間: 2025-4-1 18:57
,Mixing Histopathology Prototypes into?Robust Slide-Level Representations for?Cancer Subtyping,house malignant lymphoma dataset show comparable performance to current state-of-the-art methods, while achieving lower training costs in terms of computational time and memory load. Code is publicly available at ..作者: exophthalmos 時(shí)間: 2025-4-1 23:36 作者: jumble 時(shí)間: 2025-4-2 05:36
