Retrieval-Augmented Multiple Instance Learning
Authors: Yufei CUI, Ziquan Liu, Yixin Chen, Yuchen Lu, Xinyue Yu, Xue (Steve) Liu, Tei-Wei Kuo, Miguel Rodrigues, Chun Jason Xue, Antoni Chan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations conducted on WSI classification demonstrate that the proposed RAM-MIL framework achieves state-of-the-art performance in both in-domain scenarios, where the training and retrieval data are in the same domain, and more crucially, in out-of-domain scenarios, where the (unlabeled) retrieval data originates from a different domain. and 5 Experiments The proposed methodology is evaluated on whole slide image (WSI) datasets (Camelyon16 [43, 13], Camelyon17 [12], TCGA-NSCLC, CPTAC-UCEC and CPTAC-LSCC) and general MIL datasets (See results in supplemental). |
| Researcher Affiliation | Academia | 1Mila, Mc Gill University 2University College London 3City University of Hong Kong 4 Mila, Université de Montréal 5National Taiwan University 6MBZUAI |
| Pseudocode | Yes | Algorithm 1 Retrieval-Aug Mented Multiple Instance Learning (RAM-MIL) Algorithm |
| Open Source Code | Yes | Code can be found at https://github.com/ralphc1212/ram-mil. |
| Open Datasets | Yes | The proposed methodology is evaluated on whole slide image (WSI) datasets (Camelyon16 [43, 13], Camelyon17 [12], TCGA-NSCLC, CPTAC-UCEC and CPTAC-LSCC) and general MIL datasets (See results in supplemental). |
| Dataset Splits | Yes | Each result is obtained with 10-fold splits of training/validation/testing sets. and To ensure robustness and avoid the influence of outliers, each experiment is executed 10 times on randomly partitioned train/validation/test sets. |
| Hardware Specification | No | The paper does not explicitly specify the hardware used for experiments, such as specific GPU models, CPU models, or memory configurations. |
| Software Dependencies | No | The paper mentions the use of "Adam optimizer" but does not specify version numbers for any software dependencies like programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | For the backbone MIL model we use the the same parameter setup as CLAM. The model parameters are updated via the Adam optimizer with an L2 weight decay of 1e-5 and a learning rate of 2e-4. and Finally, we train a single logistic regression classifier using the merged representation. The Adam optimizer is used to update the model parameters, with a L2 weight decay of 1e-4 and a learning rate of 2e-4. The models are trained for a minimum of 40 epochs and up to a maximum of 200 epochs if the early stopping criterion is not met. |