Samba: Severity-aware Recurrent Modeling for Cross-domain Medical Image Grading
Authors: Qi Bi, Jingjun Yi, Hao Zheng, Wei Ji, Haolan Zhan, Yawen Huang, Yuexiang Li, Yefeng Zheng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the proposed Samba outperforms the VMamba baseline by an average accuracy of 23.5%, 5.6% and 4.1% on the cross-domain grading of fatigue fracture, breast cancer and diabetic retinopathy, respectively. |
| Researcher Affiliation | Collaboration | Qi Bi1 , Jingjun Yi2, Hao Zheng2 , Wei Ji3, Haolan Zhan4, Yawen Huang2, Yuexiang Li5 , Yefeng Zheng1 1Westlake University, China, 2Jarvis Research Center, Tencent Youtu Lab, China, 3Yale University, United States, 4Monash University, Australia, 5Guangxi Medical University, China |
| Pseudocode | No | The paper describes its methodology in detail using prose and diagrams (e.g., Figure 2) but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | Source code is available at https://github.com/Bi Qi WHU/Samba. |
| Open Datasets | Yes | Cross-domain Fatigue Fracture Grading Benchmark [31]... Cross-domain Breast Cancer Grading Benchmark...2https://github.com/YANRUI121/Breast-cancer-grading... Cross-domain Diabetic Retinopathy Grading Benchmark consists of six DR retinal image datasets, namely, Deep DR [33], Messidor [1], IDRID [40], APTOS [3], FGADR [67], and RLDR [49]. |
| Dataset Splits | No | The paper defines source and target domains for cross-domain generalization and mentions the use of training and testing data. However, it does not specify explicit train/validation/test splits (e.g., percentages or sample counts) within these domains for reproducibility. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or memory used for running the experiments. It mentions GFLOPs as a model metric, not hardware. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, frameworks) used in the experiments. |
| Experiment Setup | Yes | In the EM algorithm, the iteration number T plays an important role... we report the results when the iteration number T of the EM algorithm varies from 1 to 8... By default K is set to 64, and we further test the performance when K is set to 16, 32, 48 and 96, respectively... Using moving average to update the severity base µk is able to improve the performance substantially. |