Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation
Authors: Ziyang Chen, Yiwen Ye, Yongsheng Pan, Yong Xia
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments establish the effectiveness of the proposed gradient alignment and dynamic learning rate and substantiate the superiority of our Gra Ta method over other state-of-the-art TTA methods on a benchmark medical image segmentation task. ... We evaluate our proposed Gra Ta and other state-of-the-art TTA methods on the joint optic disc (OD) and cup (OC) segmentation task, which comprises five public datasets collected from different medical centres ... We utilize the Dice score metric (DSC) for evaluation. |
| Researcher Affiliation | Academia | 1 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi an, China 2 Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China 3 Ningbo Institute of Northwestern Polytechnical University, Ningbo, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: The Algorithm of Gra Ta. |
| Open Source Code | Yes | Code https://github.com/Chen-Ziyang/Gra Ta |
| Open Datasets | Yes | We evaluate our proposed Gra Ta and other state-of-the-art TTA methods on the joint optic disc (OD) and cup (OC) segmentation task, which comprises five public datasets collected from different medical centres, denoted as domain A (RIM-ONE-r3 (Fumero et al. 2011)), B (REFUGE (Orlando et al. 2020)), C (ORIGA (Zhang et al. 2010)), D (REFUGE-Validation/Test (Orlando et al. 2020)), and E (Drishti-GS (Sivaswamy et al. 2014)). |
| Dataset Splits | No | The paper describes using entire domains as source or target for training and testing, but does not provide specific percentages or counts for training, validation, and test splits within each dataset or domain. For example, it states: "We trained a Res UNet-34 (...) as the baseline individually on each domain (source domain) and subsequently tested it on each remaining domain (target domain)." |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer and Res UNet-34 backbone, but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | For a fair comparison, we conducted single-iteration adaptation for each batch of test data using a batch size of 1 across all experiments following (Yang et al. 2022). ... The scaling factor β is set to 0.0001 empirically. |