Deep Neural Networks:神经网络进行端到端的训练,使用LSTM、卷积层、注意力层、门机制、双线性融合等设计序列数据或图像数据的复杂交互。 Multiple Kernel learning:多核学习(将不同的核用于不同的数据模态/视图) Graphical models:利用隐马尔可夫模型或贝叶斯网络建模数据的联合概率分布(生成式)或条件概率(判别式) 4.5...
Multimodal Deep Learning models typically consist of multipleneural networks, each specialized in analyzing a particular modality. The output of these networks is then combined using various fusion techniques, such as early fusion, late fusion, or hybrid fusion, to create a joint representation of the...
In recent years, deep learning methods have been widely used in the field of medical image processing [1,2,3,4,5]. For the diagnosis of many types of diseases, multiple forms of data are often required to be considered together, such as textual information (clinical presentation, past medic...
Internal cross validation results for individual data modality to predict Alzheimer’s stage (a) Imaging results: deep learning prediction performs better than shallow learning predictions (b) EHR results: deep learning outperforms shallow models kNN and SVM and is comparable to decision trees and ran...
to provide explanations for the prediction using the deep learning model. However, in our study, it was difficult to apply visual explanation on MR images to interpret the decision-making of deep learning models because of the complicated model architectures for multimodal fusion and feature extractio...
Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based models to solve different multimodal problems such as multimodal representation learning, multimodal fusion for downstream tasks e.g., multimodal sentiment analy...
The endoscopic image plus clinical information group was statistically more significant than the other models. This model focused more on the tumor when trained with clinical information.The deep-learning model developed suggests that gastrointestinal endoscopic imaging, in combination with other clinical ...
指令学习与模型微调 Prompt Learning and Model Finetuning Pfedprompt: Learning personalized prompt for vision-language models in federated learning T Guo, S Guo, J Wang WWW, 2023 PUB Global and Local Prompts Cooperation via Optimal Transport for Federated Learning H Li, W Huang, J Wang, Y Shi ...
A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in PytorchDocumentation: https://pytorch-widedeep.readthedocs.ioCompanion posts and tutorials: infinitomlExperiments and comparison with LightGBM: TabularDL vs LightGBMSlack...
G. Positional SHAP (PoSHAP) for interpretation of machine learning models trained from biological sequences. PLoS Comput. Biol. 18, e1009736 (2022). Article Google Scholar Steyaert, S. et al. Multimodal data fusion of adult and pediatric brain tumors with deep learning. Preprint at medRxiv...