Attention-based Deep Multiple Instance Learning
Authors: Maximilian Ilse, Jakub Tomczak, Max Welling
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability. |
| Researcher Affiliation | Academia | 1University of Amsterdam, the Netherlands. |
| Pseudocode | No | The paper does not contain any figures, blocks, or sections explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper mentions using code for a comparison method ('We use code provided with (Doran & Ray, 2014): https: //github.com/garydoranjr/misvm'), but it does not provide an unambiguous statement or link for the open-sourcing of the authors' own described methodology. |
| Open Datasets | Yes | We evaluate our approach on a number of different MIL datasets: five MIL benchmark datasets (MUSK1, MUSK2, FOX, TIGER, ELEPHANT), an MNIST-based image dataset (MNIST-BAGS) and two real-life histopathology datasets (BREAST CANCER, COLON CANCER). The MNIST dataset is well-known, and the histopathology datasets are cited with proper bibliographic information (Gelasca et al., 2008 and Sirinukunwattana et al., 2016). |
| Dataset Splits | Yes | In order to obtain a fair comparison we use a common evaluation methodology, i.e., 10-fold-cross-validation, and five repetitions per experiment. If an attention-based MIL pooling layer is used the number of parameters in V was determined using a validation set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory specifications, or cloud computing resources used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of the Adam optimization algorithm and refers to third-party code for a comparison method, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | In order to obtain a fair comparison we use a common evaluation methodology, i.e., 10-fold-cross-validation, and five repetitions per experiment. If an attention-based MIL pooling layer is used the number of parameters in V was determined using a validation set. We tested the following dimensions (L): 64, 128 and 256. Finally, all layers were initialized according to Glorot & Bengio (2010) and biases were set to zero. The models are trained with the Adam optimization algorithm (Kingma & Ba, 2014). We keep the default parameters for β1 and β2, see Table 10 in the Appendix. |