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Found 10 papers, 0 papers with code
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models
The best candidate is finally selected from this set based on the BLEURT score.
Object Topological Character Acquisition by Inductive Learning
Understanding the shape and structure of objects is undoubtedly extremely important for object recognition, but the most common pattern recognition method currently used is machine learning, which often requires a large number of training data.
Multi-modal learning for predicting the genotype of glioma
In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks.
Predicting conversion of mild cognitive impairment to Alzheimer's disease
In this study, we develop a self-supervised contrastive learning approach to generate structural brain networks from routine anatomical MRI under the guidance of diffusion MRI.
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading
In this proposed scheme, we design a normalized modality contrastive loss (NMC-loss), which could promote to disentangle multi-modality complementary representation of FFPE and frozen sections from the same patient.
Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma
Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone.
Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks
Here we propose a method to predict IDH mutation using GNN, based on the structural brain network of patients.
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction.
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks.
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma
EMReDL showed to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration.
