Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation
Authors: Soopil Kim, Sion An, Philip Chikontwe, Sang Hyun Park1808-1816
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed model using three 3D CT datasets with annotations of different organs. Our model yielded significantly improved performance over state-of-theart few shot segmentation models and was comparable to a fully supervised model trained with more target training data. |
| Researcher Affiliation | Academia | Soopil Kim, Sion An, Philip Chikontwe, Sang Hyun Park Department of Robotics Engineering, DGIST soopilkim, sion an, philipchicco, shpark13135@dgist.ac.kr |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The proposed method was evaluated on the Multi-atlas labeling Beyond the Cranial Vault (BCV) dataset (Landman et al. 2015)... We employed CTORG (Blaine Rister and Rubin 2019)... Second, we also evaluated our method on the DECATHLON(Simpson et al. 2019) dataset. |
| Dataset Splits | Yes | The BCV dataset was divided into 15 volumes for training or selecting support data, 5 volumes for validation, and 10 volumes for testing for each organ. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like Image Net VGG16, Adam optimization, and U-Net but does not provide specific version numbers for any libraries or solvers needed to replicate the experiment. |
| Experiment Setup | Yes | He initialization (He et al. 2015) was used for all models with Adam (Kingma and Ba 2014) optimization and a learning rate of 10 4. For every iteration in the training stage, support and query volumes were randomly selected from training data containing various organ segmentation labels except the target organ. For the bidirectional GRU models, a total 5 slices were feed into the model, i.e., na was set as 2. |