SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
Authors: Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng, Qianni Cao, Jinyuan Qu, Jinli Suo, Qionghai Dai
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show SCI s superior performance to state-of-the-art methods including commercial compressors, data-driven ones, and INR based counterparts on diverse biomedical data. Experimentally, we evaluated SCI s performance and validated its wide applicability on biological and medical data. |
| Researcher Affiliation | Academia | Runzhao Yang1, Tingxiong Xiao1, Yuxiao Cheng1, Qianni Cao2, Jinyuan Qu1, Jinli Suo1, Qionghai Dai1 1Department of Automation, Tsinghua University, Beijing 100084, China 2Department of Electrical Engineering, Tsinghua University, Beijing 100084, China |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The source code can be found at https: //github.com/Richeal Young/Implicit Neural Compression.git. |
| Open Datasets | Yes | For medical data we used Hi P-CT (Walsh et al. 2021), a public dataset containing data volumes of human organs from the tissue to cellular scales, which are of large diversity in both appearance (e.g., structure, contrast, brightness, and noise levels). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits or cross-validation details for its own method. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or processor types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like OpenCV, FFmpeg, Pytorch, and Compress AI but does not provide specific version numbers for these or other critical software dependencies. |
| Experiment Setup | Yes | The network includes 7 layers, fr = 2.2, the hyper parameter of sinusoidal frequency w0 = 20, and is optimized by Adamax (Kingma and Ba 2014) with learning rate of 0.001. During partitioning, we set M = 1 and amax = 50 by default empirically. |