5 citations · 6 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection
Akhila Krishna, Ravi Kant Gupta, Pranav Jeevan +1
Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients. This paper…
Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification
Amruta Parulekar, Utkarsh Kanwat, Ravi Kant Gupta +5
Segmentation and classification of cell nuclei in histopathology images using deep neural networks (DNNs) can save pathologists' time for diagnosing various diseases, including can…
Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination
Ravi Kant Gupta, Shounak Das, Amit Sethi
This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Existing adversarial domain adaptation methods may not effecti…