no code implementations • 4 Mar 2024 • Akhila Krishna, Ravi Kant Gupta, Pranav Jeevan, Amit Sethi
Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients.
no code implementations • 5 Oct 2023 • Amruta Parulekar, Utkarsh Kanwat, Ravi Kant Gupta, Medha Chippa, Thomas Jacob, Tripti Bameta, Swapnil Rane, Amit Sethi
We propose a method to train DNNs for instance segmentation and classification on multiple datasets where the set of classes across the datasets are related but not the same.
no code implementations • 29 Sep 2023 • Ravi Kant Gupta, Shounak Das, Amit Sethi
This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images.
no code implementations • 16 Jul 2023 • Akhila Krishna K, Ravi Kant Gupta, Nikhil Cherian Kurian, Pranav Jeevan, Amit Sethi
The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection.
no code implementations • 19 Apr 2023 • Chirag P, Mukta Wagle, Ravi Kant Gupta, Pranav Jeevan, Amit Sethi
We propose a new technique called CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation.
Ranked #1 on Unsupervised Domain Adaptation on FHIST
no code implementations • 26 Aug 2022 • Ravi Kant Gupta, Shivani Nandgaonkar, Nikhil Cherian Kurian, Swapnil Rane, Amit Sethi
With our pipeline, we achieved an average area under the curve (AUC) of 0. 964 for tumor detection, and 0. 942 for histological subtyping between adenocarcinoma and squamous cell carcinoma on the TCGA dataset.