9 citations · 9 across the 8 of their papers we have counts for
8 papers
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget
Sourajit Saha, Shaswati Saha, Md Osman Gani +2
Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN)architectures with downstream and upstream propagation flow are p…
Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)
Sumeet Menon, David Chapman
Semi-supervised learning is the problem of training an accurate predictive model by combining a small labeled dataset with a presumably much larger unlabeled dataset. Many methods…
Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation
Nishanjan Ravin, Sourajit Saha, Alan Schweitzer +4
We demonstrate that Domain Invariant Feature Learning (DIFL) can improve the out-of-domain generalizability of a deep learning Tuberculosis screening algorithm. It is well known th…
CCS-GAN: COVID-19 CT-scan classification with very few positive training images
Sumeet Menon, Jayalakshmi Mangalagiri, Josh Galita +7
We present a novel algorithm that is able to classify COVID-19 pneumonia from CT Scan slices using a very small sample of training images exhibiting COVID-19 pneumonia in tandem wi…
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
Jayalakshmi Mangalagiri, David Chapman, Aryya Gangopadhyay +7
We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated a…
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs
Kushal Mehta, Arshita Jain, Jayalakshmi Mangalagiri +3
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm…