ConDSeg: A General Medical Image Segmentation Framework via Contrast-Driven Feature Enhancement
Authors: Mengqi Lei, Haochen Wu, Xinhua Lv, Xin Wang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five datasets across three scenarios demonstrate the state-of-the-art performance of our method, proving its advanced nature and general applicability to various medical image segmentation scenarios. |
| Researcher Affiliation | Collaboration | Mengqi Lei1, Haochen Wu1, Xinhua Lv1, Xin Wang2 1China University of Geosciences, Wuhan 430074, China 2Baidu Inc, Beijing, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in text and block diagrams but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/Mengqi-Lei/Con DSeg |
| Open Datasets | Yes | We conducted experiments on five challenging public datasets: Kvasir-SEG (Jha et al. 2020), Kvasir-Sessile (Jha et al. 2021), Gla S (Sirinukunwattana et al. 2017), ISIC2016 (Gutman et al. 2016), and ISIC-2017 (Codella et al. 2018), covering subdivision tasks across three medical image modalities. |
| Dataset Splits | No | Detailed information about the datasets is shown in the Supplementary Material. |
| Hardware Specification | Yes | All experiments were conducted on an NVIDIA Ge Force RTX 4090 GPU, with the image size adjusted to 256 256 pixels. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and Res Net-50 as the default encoder, but does not provide specific version numbers for any software dependencies like programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | The batch size was set to 4, and the Adam optimizer (Kingma and Ba 2014) was used for optimization. We use the Res Net-50 (He et al. 2016) as the default encoder... In the first stage, the learning rate is set to 1e-4. In the second stage, we load the weights of the Encoder and set its learning rate to a lower 1e-5, while for the rest of the network, the learning rate is set to 1e-4. The window size for CDFA is set to 3. |