10 papers
Few-Shot Cross-Dataset Adaptation for Tuberculosis Detection Using DenseNet
Bidhan Biswas, Shahadat Hossain Sohag, Nabil Ashab +2
Tuberculosis (TB) is one of the most common and dangerous bacterial ailments. Every year, it causes a large number of deaths worldwide. Although many deep learning models can detec…
MagViT: Interpretable Multi-Magnification Transformers with Patient-Level Model Selection for Breast Histopathology
Nabil Ashab, Soumit Kumar Kundu, Saif Mahmud Parvez +3
Breast cancer is one of the most common types of cancer among women around the world. Rapid detection and early treatment can hinder its progress to more complex stages and can imp…
ORViT-DR: Ordinally-Robust Hybrid ViT for Low-Resolution Diabetic Retinopathy Grading
Soumit Kumar Kundu, Nabil Ashab, Bidhan Biswas +4
Diabetic retinopathy (DR) is one of the main causes of impaired vision. A good and reliable automated grading system can make the screening process safer and more accurate. Because…
VSANet: View-aware Sparse Attention Network for Light Field Image Denoising
Gargi Panda, Soumitra Kundu, Saumik Bhattacharya +1
Light field (LF) image denoising is challenging due to the high-dimensional structure of LF data. While noise is independent across sub-aperture images, scene content exhibits stro…
l0-Regularized Sparse Coding-based Interpretable Network for Multi-Modal Image Fusion
Gargi Panda, Soumitra Kundu, Saumik Bhattacharya +1
Multi-modal image fusion (MMIF) enhances the information content of the fused image by combining the unique as well as common features obtained from different modality sensor image…
Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximization
Gargi Panda, Soumitra Kundu, Saumik Bhattacharya +1
Hyperspectral unmixing (HSU) aims to separate each pixel into its constituent endmembers and estimate their corresponding abundance fractions. This work presents an algorithm-unrol…