6 citations · 12 across the 7 of their papers we have counts for
7 papers
Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size
Parisa Boodaghi Malidarreh, Jillur Rahman Saurav, Thuong Le Hoai Pham +5
Vector Quantization (VQ) techniques face significant challenges in codebook utilization, limiting reconstruction fidelity in image modeling. We introduce a Dual Codebook mechanism…
Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression
Neel R Vora, Amir Hajighasemi, Cody T. Reynolds +16
Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorde…
SpatialVisVR: An Immersive, Multiplexed Medical Image Viewer With Contextual Similar-Patient Search
Jai Prakash Veerla, Partha Sai Guttikonda, Amir Hajighasemi +7
In contemporary pathology, multiplexed immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) present both significant opportunities and challenges. These methodologies…
The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection
Michael Robben, Amir Hajighasemi, Mohammad Sadegh Nasr +9
Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of…
Multimodal Pathology Image Search Between H&E Slides and Multiplexed Immunofluorescent Images
Amir Hajighasemi, MD Jillur Rahman Saurav, Mohammad S Nasr +6
We present an approach for multimodal pathology image search, using dynamic time warping (DTW) on Variational Autoencoder (VAE) latent space that is fed into a ranked choice voting…
Histopathology Slide Indexing and Search: Are We There Yet?
Helen H. Shang, Mohammad Sadegh Nasr, Jai Prakash Veerla +7
The search and retrieval of digital histopathology slides is an important task that has yet to be solved. In this case study, we investigate the clinical readiness of three state-o…
Clinically Relevant Latent Space Embedding of Cancer Histopathology Slides through Variational Autoencoder Based Image Compression
Mohammad Sadegh Nasr, Amir Hajighasemi, Paul Koomey +6
In this paper, we introduce a Variational Autoencoder (VAE) based training approach that can compress and decompress cancer pathology slides at a compression ratio of 1:512, which…