Search Results for author: Amit Sethi

Found 21 papers, 8 papers with code

Convolutional Xformers for Vision

1 code implementation25 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.

Image Classification

Perceptual cGAN for MRI Super-resolution

no code implementations23 Jan 2022 Sahar Almahfouz Nasser, Saqib Shamsi, Valay Bundele, Bhavesh Garg, Amit Sethi

Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients.

Super-Resolution

Memory Efficient Adaptive Attention For Multiple Domain Learning

no code implementations21 Oct 2021 Himanshu Pradeep Aswani, Abhiraj Sunil Kanse, Shubhang Bhatnagar, Amit Sethi

Training CNNs from scratch on new domains typically demands large numbers of labeled images and computations, which is not suitable for low-power hardware.

WaveMix: Multi-Resolution Token Mixing for Images

1 code implementation29 Sep 2021 Pranav Jeevan P, Amit Sethi

Our work suggests that research on model structures that exploit the right inductive bias is far from over, and that such models can enable the training of computer vision models in settings with limited GPU resources.

Image Classification

Vision Xformers: Efficient Attention for Image Classification

2 code implementations5 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.

Image Classification

Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images

no code implementations30 Nov 2020 Abhijeet Patil, Mohd. Talha, Aniket Bhatia, Nikhil Cherian Kurian, Sammed Mangale, Sunil Patel, Amit Sethi

Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different.

PAL : Pretext-based Active Learning

no code implementations29 Oct 2020 Shubhang Bhatnagar, Sachin Goyal, Darshan Tank, Amit Sethi

To counter the paucity of data, we also deploy another head on the scoring network for regularization via multi-task learning and use an unusual self-balancing hybrid scoring function.

Active Learning Multi-Task Learning +2

Activation Functions: Do They Represent A Trade-Off Between Modular Nature of Neural Networks And Task Performance

1 code implementation16 Sep 2020 Himanshu Pradeep Aswani, Amit Sethi

Current research suggests that the key factors in designing neural network architectures involve choosing number of filters for every convolution layer, number of hidden neurons for every fully connected layer, dropout and pruning.

Switching Loss for Generalized Nucleus Detection in Histopathology

no code implementations9 Aug 2020 Deepak Anand, Gaurav Patel, Yaman Dang, Amit Sethi

Remarkably, without retraining on target datasets, our pre-trained nucleus detector also outperformed existing nucleus detectors that were trained on at least some of the images from the target datasets.

whole slide images

Visualization for Histopathology Images using Graph Convolutional Neural Networks

no code implementations16 Jun 2020 Mookund Sureka, Abhijeet Patil, Deepak Anand, Amit Sethi

With the increase in the use of deep learning for computer-aided diagnosis in medical images, the criticism of the black-box nature of the deep learning models is also on the rise.

Graph Convolutional Network

Pixel-wise Segmentation of Right Ventricle of Heart

no code implementations21 Aug 2019 Yaman Dang, Deepak Anand, Amit Sethi

One of the first steps in the diagnosis of most cardiac diseases, such as pulmonary hypertension, coronary heart disease is the segmentation of ventricles from cardiac magnetic resonance (MRI) images.

Histographs: Graphs in Histopathology

no code implementations14 Aug 2019 Shrey Gadiya, Deepak Anand, Amit Sethi

Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing cancers.

Fast GPU-Enabled Color Normalization for Digital Pathology

1 code implementation10 Jan 2019 Goutham Ramakrishnan, Deepak Anand, Amit Sethi

Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology.

whole slide images

Some New Layer Architectures for Graph CNN

no code implementations31 Oct 2018 Shrey Gadiya, Deepak Anand, Amit Sethi

While convolutional neural networks (CNNs) have recently made great strides in supervised classification of data structured on a grid (e. g. images composed of pixel grids), in several interesting datasets, the relations between features can be better represented as a general graph instead of a regular grid.

General Classification

Classification of Breast Cancer Histology using Deep Learning

1 code implementation22 Feb 2018 Aditya Golatkar, Deepak Anand, Amit Sethi

In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al.

General Classification

Deep Learning-Based Image Kernel for Inductive Transfer

no code implementations13 Dec 2015 Neeraj Kumar, Animesh Karmakar, Ranti Dev Sharma, Abhinav Mittal, Amit Sethi

We propose a method to classify images from target classes with a small number of training examples based on transfer learning from non-target classes.

Transfer Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.