5 citations · 6 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
Classification and Morphological Analysis of DLBCL Subtypes in H\&E-Stained Slides
Ravi Kant Gupta, Mohit Jindal, Garima Jain +10
We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-…
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…
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…