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Found 6 papers, 0 papers with code
Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection
Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients.
Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification
We propose a method to train DNNs for instance segmentation and classification on multiple datasets where the set of classes across the datasets are related but not the same.
Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination
This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images.
Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis
The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection.
CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation
We propose a new technique called CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation.
Ranked #1 on
Unsupervised Domain Adaptation
on FHIST
EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning
With our pipeline, we achieved an average area under the curve (AUC) of 0. 964 for tumor detection, and 0. 942 for histological subtyping between adenocarcinoma and squamous cell carcinoma on the TCGA dataset.
