12 citations · 40 across the 18 of their papers we have counts for
20 papers
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…
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…
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…
WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency
Pranav Jeevan, Neeraj Nixon, Amit Sethi
Recent advancements in single image super-resolution have been predominantly driven by token mixers and transformer architectures. WaveMixSR utilized the WaveMix architecture, empl…
Normalizing Flow-Based Metric for Image Generation
Pranav Jeevan, Neeraj Nixon, Amit Sethi
We propose two new evaluation metrics to assess realness of generated images based on normalizing flows: a simpler and efficient flow-based likelihood distance (FLD) and a more exa…