Predictive Accuracy-Based Active Learning for Medical Image Segmentation
Authors: Jun Shi, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Hong An, Xudong Xue, Bing Yan
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results on multiple datasets demonstrate the superiority of PAAL. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, University of Science and Technology of China 2School of Data Science, University of Science and Technology of China 3Laoshan Laboratory 4Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology 5Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 The Proposed PAAL Process |
| Open Source Code | Yes | The code is available at https://github.com/ shijun18/PAAL-Med Seg. |
| Open Datasets | Yes | As shown in Table 1, datasets used in our experiments include (1) Brain Tumour [Antonelli et al., 2022]: a multi-modal Magnetic Resonance (MR) dataset provided by the Medical Segmentation Decathlon (MSD)...; (2) Seg THOR [Lambert et al., 2020]: a chest Computed Tomography (CT) dataset...; (3) ACDC [Bernard et al., 2018]: a commonly used cardiac MR dataset... |
| Dataset Splits | Yes | During training, each dataset was split into training and validation sets at a ratio of 8:2 for five-fold cross-validation. |
| Hardware Specification | Yes | All models are trained from scratch on 8 NVIDIA A800 GPUs |
| Software Dependencies | No | The paper states that "PAAL and all baselines are implemented using Py Torch", but it does not specify the version number for PyTorch or any other software libraries or dependencies with specific version numbers. |
| Experiment Setup | Yes | All models are trained from scratch on 8 NVIDIA A800 GPUs, with the same loss function, e.g. the combined loss [Shi et al., 2023] of Dice and Cross-Entropy for segmentation model and MSE loss for AP. We set 3 maximum querying ratios for different datasets according to varying slice scales: {5%, 10%, 20%} for the Brain Tumour dataset and {10%, 20%, 50%} for the other datasets. The maximum iterations for each dataset are related to the maximum querying ratio. Specifically, Brain Tumour dataset has maximum iterations of {10, 15, 15}, while the other datasets have the same {5, 15, 20}. For the Brain Tumour dataset, the slice resolution is resized to 4 256 256, while for the other datasets, it is 1 512 512. We employ Adam W optimizer [Loshchilov and Hutter, 2018] with an initial learning rate of 1e-3, a batch size of 64, and use the cosine annealing strategy [Loshchilov and Hutter, 2016] to control the learning rate, with a weight decay of 1e-4, warm-up epochs of 10, and the minimum learning rate of 1e-6. Each model is evaluated on the validation set at the end of every epoch. To alleviate overfitting, we adopt an early stopping strategy with a tolerance of 40 epochs to search for the best model within 400 epochs and apply data augmentation, including random distortion, rotation, flip, and noise. |