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 • 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.
1 code implementation • 1 Jul 2023 • Pranav Jeevan, Akella Srinidhi, Pasunuri Prathiba, Amit Sethi
We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture which uses a 2D-discrete wavelet transform for spatial token-mixing.
Ranked #1 on Image Super-Resolution on BSD100 - 2x upscaling
1 code implementation • 1 Jul 2023 • Pranav Jeevan, Dharshan Sampath Kumar, Amit Sethi
The current state-of-the-art models for image inpainting are computationally heavy as they are based on transformer or CNN backbones that are trained in adversarial or diffusion settings.
Ranked #1 on Image Inpainting on ImageNet
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 • 22 Feb 2023 • Pranav Jeevan, Nikhil Cherian Kurian, Amit Sethi
Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images.
Ranked #1 on Image Classification on BreakHis
Breast Cancer Histology Image Classification Image Classification
1 code implementation • 28 May 2022 • Pranav Jeevan, Kavitha Viswanathan, Anandu A S, Amit Sethi
The whole architecture is a stack of self-similar and resolution-preserving WaveMix blocks, which allows architectural flexibility for various tasks and levels of resource availability.
Ranked #1 on Image Classification on Galaxy10 DECals (using extra training data)
1 code implementation • 7 Mar 2022 • Pranav Jeevan, Amit Sethi
The multi-scale nature of the DWT also reduces the requirement for a deeper architecture compared to the CNNs, as the latter relies on pooling for partial spatial mixing.
Ranked #14 on Image Classification on MNIST
1 code implementation • 25 Jan 2022 • Pranav Jeevan, Amit Sethi
Vision transformers (ViTs) have found only limited practical use in processing images, in spite of their state-of-the-art accuracy on certain benchmarks.
Ranked #22 on Image Classification on Tiny ImageNet Classification
1 code implementation • 25 Oct 2021 • Anirudh Mittal, Pranav Jeevan, Prerak Gandhi, Diptesh Kanojia, Pushpak Bhattacharyya
We devise a novel scoring mechanism to annotate the training data with a humour quotient score using the audience's laughter.
2 code implementations • 5 Jul 2021 • Pranav Jeevan, Amit Sethi
Secondly, we introduced an inductive bias for images by replacing the initial linear embedding layer by convolutional layers in ViX, which significantly increased classification accuracy without increasing the model size.
Ranked #206 on Image Classification on CIFAR-10 (using extra training data)