Conclave
发表于 2025-3-30 10:55:44
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
内向者
发表于 2025-3-30 14:59:36
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
生气的边缘
发表于 2025-3-30 18:32:02
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Cacophonous
发表于 2025-3-31 00:29:51
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英寸
发表于 2025-3-31 03:49:10
,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
大喘气
发表于 2025-3-31 05:51:30
,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
可用
发表于 2025-3-31 12:27:23
,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
nephritis
发表于 2025-3-31 17:14:23
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Perigee
发表于 2025-3-31 18:27:22
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Hangar
发表于 2025-4-1 00:48:22
,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