12 citations · 14 across the 5 of their papers we have counts for
5 papers
Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis
Arjun Krishna, Ge Wang, Klaus Mueller
Medical imaging applications are highly specialized in terms of human anatomy, pathology, and imaging domains. Therefore, annotated training datasets for training deep learning app…
Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation
Soham Bhosale, Arjun Krishna, Ge Wang +1
Medical Image Segmentation is a useful application for medical image analysis including detecting diseases and abnormalities in imaging modalities such as MRI, CT etc. Deep learnin…
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning
Arjun Krishna, Kedar Bartake, Chuang Niu +4
Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics. These merits, however, stand at great odds w…
Noise Entangled GAN For Low-Dose CT Simulation
Chuang Niu, Ge Wang, Pingkun Yan +8
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT…
Video Segment Copy Detection Using Memory Constrained Hierarchical Batch-Normalized LSTM Autoencoder
Arjun Krishna, A S Akil Arif Ibrahim
In this report, we introduce a video hashing method for scalable video segment copy detection. The objective of video segment copy detection is to find the video (s) present in a l…