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…
IDAL: Improved Domain Adaptive Learning for Natural Images Dataset
Ravi Kant Gupta, Shounak Das, Amit Sethi
We present a novel approach for unsupervised domain adaptation (UDA) for natural images. A commonly-used objective for UDA schemes is to enhance domain alignment in representation…
Predicting Genetic Mutations from Single-Cell Bone Marrow Images in Acute Myeloid Leukemia Using Noise-Robust Deep Learning Models
Garima Jain, Ravi Kant Gupta, Priyansh Jain +5
In this study, we propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images of blasts, tackling challenges…
Scalable Whole Slide Image Representation Using K-Mean Clustering and Fisher Vector Aggregation
Ravi Kant Gupta, Shounak Das, Ardhendu Sekhar +1
Whole slide images (WSIs) are high-resolution, gigapixel sized images that pose significant computational challenges for traditional machine learning models due to their size and h…