7 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…