5 papers
Survival Modeling from Whole Slide Images via Patch-Level Graph Clustering and Mixture Density Experts
Ardhendu Sekhar, Vasu Soni, Keshav Aske +3
We propose a modular framework for predicting cancer specific survival directly from whole slide pathology images (WSIs). The framework consists of four key stages designed to capt…
Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images
Ardhendu Sekhar, Vasu Soni, Keshav Aske +3
Accurate survival prediction from histopathology whole-slide images (WSIs) remains challenging due to their gigapixel resolution, strong spatial heterogeneity, and complex survival…
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
Pranav Jeevan, Amit Sethi
In contemporary computer vision applications, particularly image classification, architectural backbones pre-trained on large datasets like ImageNet are commonly employed as featur…
Evaluation Metric for Quality Control and Generative Models in Histopathology Images
Pranav Jeevan, Neeraj Nixon, Abhijeet Patil +1
Our study introduces ResNet-L2 (RL2), a novel metric for evaluating generative models and image quality in histopathology, addressing limitations of traditional metrics, such as Fr…
FLD+: Data-efficient Evaluation Metric for Generative Models
Pranav Jeevan, Neeraj Nixon, Amit Sethi
We introduce a new metric to assess the quality of generated images that is more reliable, data-efficient, compute-efficient, and adaptable to new domains than the previous metrics…