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..
Class-Aware Adversarial Transformers for Medical Image Segmentation
Authors: Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, James Duncan
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that CASTformer dramatically outperforms previous state-of-the-art transformer-based approaches on three benchmarks, obtaining 2.54%-5.88% absolute improvements in Dice over previous models. |
| Researcher Affiliation | Academia | Chenyu You1 Ruihan Zhao2 Fenglin Liu3 Siyuan Dong1 Sandeep Chinchali2 Ufuk Topcu2 Lawrence Staib1 James S. Duncan1 1Yale University 2UT Austin 3University of Oxford |
| Pseudocode | No | No pseudocode or algorithm block was found. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Sections 4 and supplemental material. |
| Open Datasets | Yes | Datasets. We experiment on multiple challenging benchmark datasets: Synapse1, Li TS, and MP-MRI. More details can be found in Appendix ??. 1https://www.synapse.org/#!Synapse:syn3193805/wiki/217789 |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Sections 4 and supplemental material. |
| Hardware Specification | Yes | We train all models on a single NVIDIA Ge Force RTX 3090 GPU with 24GB of memory. |
| Software Dependencies | Yes | All our experiments are implemented in Pytorch 1.7.0. |
| Experiment Setup | Yes | We utilize the Adam W optimizer [90] in all our experiments. For training our generator and discriminator, we use a learning rate of 5e 4 with a batch size of 6, and train each model for 300 epochs for all datasets. We set the sampling number n on each feature map and the total iterative number M as 16 and 4, respectively. We also adopt the input resolution and patch size P as 224 224 and 14, respectively. We set λ1 = 0.5, λ2 = 0.5, and λ3 = 0.1 in this experiments. |