1 citations · 1 across the 4 of their papers we have counts for
4 papers
PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images
Akhila Krishna, Nikhil Cherian Kurian, Abhijeet Patil +2
Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessibl…
Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection
Akhila Krishna, Ravi Kant Gupta, Pranav Jeevan +1
Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients. This paper…
Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification
Amruta Parulekar, Utkarsh Kanwat, Ravi Kant Gupta +5
Segmentation and classification of cell nuclei in histopathology images using deep neural networks (DNNs) can save pathologists' time for diagnosing various diseases, including can…
Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination
Ravi Kant Gupta, Shounak Das, Amit Sethi
This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Existing adversarial domain adaptation methods may not effecti…