Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Authors: Linhao Qu, xiaoyuan luo, Manning Wang, Zhijian Song
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five datasets demonstrate the efficiency of WENO. |
| Researcher Affiliation | Academia | Linhao Qu Digital Medical Research Center, School of Basic Medical Science, Fudan University lhqu20@fudan.edu.cn Xiaoyuan Luo Digital Medical Research Center, School of Basic Medical Science, Fudan University 19111010030@fudan.edu.cn Manning Wang Digital Medical Research Center, School of Basic Medical Science, Fudan University mnwang@fudan.edu.cn Zhijian Song Digital Medical Research Center, School of Basic Medical Science, Fudan University zjsong@fudan.edu.cn |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/miccaiif/WENO. |
| Open Datasets | Yes | We used five datasets to comprehensively evaluate the performance of WENO, including two synthetic datasets and three real-world datasets. To explore the performance of WENO under different positive instance ratios, we used the 10-class natural image dataset CIFAR 10 [15] and the 9-class pathological image dataset CRC [14] to construct synthetic WSI datasets with different positive instance ratios... Furthermore, we used real-world pathology datasets from three different medical centers to evaluate the performance of WENO, including a breast cancer lymph node metastasis public dataset, the Camelyon16 dataset [1], a lung cancer diagnosis public dataset, the TCGA Lung Cancer dataset, and an in-house cervical cancer lymph node metastasis dataset, the Clinical Cervical dataset. |
| Dataset Splits | No | For the hard positive instance mining strategy, we drop the instances with positive probability higher than a threshold in positive bags. The hyperparameter thresholds vary for each dataset, and we used grid search on the validation set to determine the optimal values. While a 'validation set' is mentioned, the paper does not specify the exact split percentages or absolute sample counts for the training, validation, and test sets to reproduce the data partitioning. |
| Hardware Specification | Yes | All experiments were performed using 4 Nvidia 3090 GPUs. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x'). It only mentions general tools like 'SGD optimizer'. |
| Experiment Setup | Yes | For the CIFAR-10-MIL dataset, the encoder in Figure 2 is implemented using the Alex Net [16]. For the other datasets, the encoder is implemented using the Res Net18 [10]. Both the prediction heads and the attention module are implemented using fully connected layers. No pre-training of the network parameters and no image augmentation are performed. The SGD optimizer is used to optimize the network parameters with a fixed learning rate of 0.001. For the hard positive instance mining strategy, we drop the instances with positive probability higher than a threshold in positive bags. The hyperparameter thresholds vary for each dataset, and we used grid search on the validation set to determine the optimal values. |