Search Results for author: Amit Sethi

Found 39 papers, 15 papers with code

Transforming Breast Cancer Diagnosis: Towards Real-Time Ultrasound to Mammogram Conversion for Cost-Effective Diagnosis

no code implementations10 Aug 2023 Sahar Almahfouz Nasser, Ashutosh Sharma, Anmol Saraf, Amruta Mahendra Parulekar, Purvi Haria, Amit Sethi

Then, we utilize generative adversarial networks (GANs) to tackle the inverse problem of generating mammogram-quality images from ultrasound images.

Domain Adaptation

Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis

no code implementations16 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.

WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting

1 code implementation1 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.

Image Inpainting

WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution

1 code implementation1 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.

Efficient Neural Network Image Super-Resolution +1

Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring

no code implementations17 Mar 2023 Nikhil Cherian Kurian, Varsha S, Abhijit PATIL, Shashikant Khade, Amit Sethi

The outlier score derived from the OOD detector is used to modulate sample selection for the subsequent semi-SL stage, ensuring that samples conforming to the distribution of the few labeled samples are more frequently exposed to the subsequent semi-SL framework.

Self-Supervised Learning whole slide images

Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers

no code implementations22 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.

Breast Cancer Histology Image Classification Image Classification

Artificial Intelligence-based Eosinophil Counting in Gastrointestinal Biopsies

no code implementations25 Nov 2022 Harsh Shah, Thomas Jacob, Amruta Parulekar, Anjali Amarapurkar, Amit Sethi

In this study, we trained and tested a deep neural network based on UNet to detect and count eosinophils in GI tract biopsies.

Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer

1 code implementation15 Sep 2022 Tirupati Saketh Chandr, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Amit Sethi

The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade.

Mitosis Detection

EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning

no code implementations26 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.

whole slide images

Shallow Water Bathymetry Survey using an Autonomous Surface Vehicle

no code implementations18 Jul 2022 Bibin Wilson, Anand Singh, Amit Sethi

We discuss the adaptation of equipment and sensors for the collection of navigation, control, and bathymetry data and also give an overview of the vehicle setup.

Deriving Surface Resistivity from Polarimetric SAR Data Using Dual-Input UNet

no code implementations5 Jul 2022 Bibin Wilson, Rajiv Kumar, Narayanarao Bhogapurapu, Anand Singh, Amit Sethi

DI-UNet uses a deep learning architecture to predict the resistivity using full polarimetric SAR data by promising a quick survey addition to the traditional method.

WaveMix: A Resource-efficient Neural Network for Image Analysis

1 code implementation28 May 2022 Pranav Jeevan, Kavitha Viswanathan, Anandu A S, Amit Sethi

We propose WaveMix -- a novel neural architecture for computer vision that is resource-efficient yet generalizable and scalable.

Efficient Neural Network Image Classification +3

WaveMix: Resource-efficient Token Mixing for Images

1 code implementation7 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.

Image Classification Inductive Bias

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

1 code implementation23 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.


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 Inductive Bias

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.

Classification Image Classification +1

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.

Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

1 code implementation16 Feb 2020 Abhijeet Patil, Dipesh Tamboli, Swati Meena, Deepak Anand, Amit Sethi

We aim to provide a better interpretation of classification results by providing localization on microscopic histopathology images.

Classification General Classification +2

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.

Classification 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

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