5 citations · 5 across the 6 of their papers we have counts for
8 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…
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…
Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images
Ardhendu Sekhar, Aditya Bhattacharya, Vinayak Goyal +4
In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train s…
HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning
Ardhendu Sekhar, Vrinda Goel, Garima Jain +5
The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in…
Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights
Ardhendu Sekhar, Ravi Kant Gupta, Amit Sethi
This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its appli…
IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence
Shreyas Chandgothia, Ardhendu Sekhar, Amit Sethi
Training a computer vision system to segment a novel class typically requires collecting and painstakingly annotating lots of images with objects from that class. Few-shot segmenta…