5 citations · 5 across the 9 of their papers we have counts for
11 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…
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…
Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images
Ravi Kant Gupta, Shounak Das, Amit Sethi
Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant…
Efficient Whole Slide Image Classification through Fisher Vector Representation
Ravi Kant Gupta, Dadi Dharani, Shambhavi Shanker +1
The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficienc…
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…