- Predicting benefit from immune checkpoint inhibitors in patients with ...🔍
- Artificial intelligence for predictive biomarker discovery in immuno ...🔍
- CT|based multimodal deep learning for non|invasive overall survival ...🔍
- Predicting Alzheimer's disease CSF core biomarkers🔍
- Cross|attention enables deep learning on limited omics|imaging ...🔍
- Artificial intelligence for biomarker discovery in Alzheimer's disease ...🔍
- Exploring non|invasive precision treatment in non|small cell lung ...🔍
- An MRI Deep Learning Model Predicts Outcome in Rectal Cancer🔍
Non|invasive multimodal CT deep learning biomarker to predict ...
Predicting benefit from immune checkpoint inhibitors in patients with ...
We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival ...
Artificial intelligence for predictive biomarker discovery in immuno ...
88. He, B.X. ∙ Zhong, Y.F. ∙ Zhu, Y.B. ... Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell ...
CT-based multimodal deep learning for non-invasive overall survival ...
CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy.
Predicting Alzheimer's disease CSF core biomarkers - Frontiers
Conclusions: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The ...
Cross-attention enables deep learning on limited omics-imaging ...
Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer ( ...
Artificial intelligence for biomarker discovery in Alzheimer's disease ...
Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data ...
Exploring non-invasive precision treatment in non-small cell lung ...
By employing a deep learning radiomics framework that can longitudinally predict diverse tumor molecular features, we further enhance the ...
An MRI Deep Learning Model Predicts Outcome in Rectal Cancer
A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification ...
Deep Multi-modal Fusion of Image and Non-image Data in Disease ...
All of them used deep learning methods to fuse image and non-image medical data for prognosis, diagnosis, or treatment prediction. This survey is organized in ...
Magnetic resonance imaging-based deep learning ... - BINASSS
The study hospital employed non-contrast CT and multi-phase CT angiography for ... DL imaging biomarker for prediction impacts clinical ...
Integrating deep and radiomics features in cancer bioimaging | bioRxiv
The network is first pretrained on head and neck tumor stage diagnosis, then finetuned on the prognostic task by internal transfer learning. In ...
Multimodal Predictive Modeling of Endovascular Treatment ...
In conclusion, integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to ...
CT-based multimodal deep learning for non-invasive overall survival ...
To develop a deep learning model combining CT scans and clinical information to predict overall survival in advanced hepatocellular carcinoma (HCC).
(PDF) Deep Learning with Multimodal Integration for Predicting ...
Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This ...
Development and validation of a multimodal neuroimaging ...
... multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis ... non-neural ...
Lotze, MD Editor-in-Chief Journal for ImmunoTherapy of Cancer. JITC Editor Picks. Non-invasive multimodal CT deep learning biomarker to predict pathological ...
Deep multimodal fusion of image and non-image data in disease ...
With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we ...
Biomarker Discovery (Image Processing/Data Analytics)
We have developed novel MRI and PET-based imaging biomarkers for detecting radiation-induced toxicity, genomics biomarkers for prediction local failure, and ...
Deep learning-based predictive model for pathological complete ...
Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based ...
Pan-cancer integrative histology-genomic analysis via multimodal ...
Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover ...