Search Results for author:
Found 8 papers, 2 papers with code
3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention
Additionally, a Pearson correlation coefficient of 0. 91 and an R^2 of 0. 89 were observed when comparing manual annotations to segmentation results for TMTV measurement.
A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images
By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC).
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation
However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease.
Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection
For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features.
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
These results, however, come with important caveats: cross-lingual models often perform best when trained on a mixture of English and high-quality monolingual NLI data (OCNLI), and are often hindered by automatically translated resources (XNLI-zh).
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
