Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Semi-supervised TEE Segmentation via Interacting with SAM Equipped with Noise-Resilient Prompting
Authors: Sen Deng, Yidan Feng, Haoneng Lin, Yiting Fan, Alex Pui-Wai Lee, Xiaowei Hu, Jing Qin
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on an in-house TEE dataset; experimental results demonstrate that our method achieves better performance than state-of-the-art SSL models. |
| Researcher Affiliation | Collaboration | 1 Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University 2 Department of cardiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China 3 Division of Cardiology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong 4 Shanghai Artificial Intelligence Laboratory |
| Pseudocode | Yes | Algorithm 1: Pseudo code of our training strategy |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The dataset consists of TEE data from 100 patients. We randomly divided the patients into three sets, with 70/30 for training and testing. |
| Dataset Splits | No | The paper states "We randomly divided the patients into three sets, with 70/30 for training and testing," but does not specify a separate validation set split or percentages for a three-way split (train/validation/test). |
| Hardware Specification | Yes | All the experiments are conducted on 4 NVIDIA 3090 GPUs. |
| Software Dependencies | No | The paper mentions that the network is optimized using Adam W, but does not specify version numbers for any software dependencies, libraries, or programming languages used. |
| Experiment Setup | Yes | The network is optimized using Adam W with a mini-batch size of 8 and trained for a total of 300 epochs. The learning rate is initialized as 0.004, which is divided by 2 every 60 epochs. The proportion of p in the SPFA algorithm and the selected points number k is set as 0.7 and 6 in our experiment. |