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Found 9 papers, 0 papers with code
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
(2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains.
Gaze Estimation Approach Using Deep Differential Residual Network
We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images.
Deep radiomic signature with immune cell markers predicts the survival of glioma patients
These results demonstrate the usefulness of proposed DRFs as non-invasive biomarker for predicting treatment response in patients with brain tumors.
Can autism be diagnosed with AI?
With AI, new radiomic models using the deep learning techniques will be also described.
Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images
Our results suggest that the proposed GMM-CNN features could improve the prediction of COVID-19 in chest computed tomography and X-ray scans.
Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients
Combined the immune markers with DRFs and clinical variables, Kaplan-Meier estimator and Log-rank test achieved the most significant difference between predicted groups of patients (short-term versus long-term survival) with p\,=\, 4. 31$\times$10$^{-7}$ compared to p\,=\, 0. 03 for Immune cell markers, p\,=\, 0. 07 for clinical variables , and p\,=\, 1. 45$\times$10$^{-5}$ for DRFs.
Future Artificial Intelligence tools and perspectives in medicine
Purpose of review: Artificial intelligence (AI) has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis.
Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma
This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM).
Modeling Information Flow Through Deep Neural Networks
This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e. g. convolutional neural networks (CNN).
