Search Results for author:
Found 14 papers, 5 papers with code
SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis
This study introduces the SHAP-integrated convolutional diagnostic network (SICDN), an interpretable feature selection method designed for limited datasets, to address the challenge posed by data privacy regulations that restrict access to medical datasets.
FAA-CLIP: Federated Adversarial Adaptation of CLIP
Despite the remarkable performance of vision language models (VLMs) such as Contrastive Language Image Pre-training (CLIP), the large size of these models is a considerable obstacle to their use in federated learning (FL) systems where the parameters of local client models need to be transferred to a global server for aggregation.
A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models
With the rapid development of artificial intelligence (AI), especially in the medical field, the need for its explainability has grown.
Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification
This paper presents simulation-based algorithms of recent DA techniques, mainly related to unsupervised domain adaptation (UDA), where labels are available only in the source domain.
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification
Federated learning (FL) has emerged as a promising approach to medical image analysis that allows deep model training using decentralized data while ensuring data privacy.
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).
