Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
Authors: Honglin Li, Yunlong Zhang, Pingyi Chen, Zhongyi Shui, Chenglu Zhu, Lin Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method, Long-contextual MIL (Long MIL), is evaluated through extensive experiments on various WSI tasks to validate its superiority in: 1) overall performance, 2) memory usage and speed, and 3) extrapolation ability compared to previous methods. |
| Researcher Affiliation | Academia | Honglin Li1,3 Yunlong Zhang1,3 Pingyi Chen1,3 Zhongyi Shui1,3 Chenglu Zhu2,3 Lin Yang2,3 1 Zhejiang University 2 Research Center for Industries of the Future and 3 School of Engineering, Westlake University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks clearly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Our code will be available at https://github.com/invoker-LL/Long-MIL. |
| Open Datasets | Yes | We use four datasets to evaluate our method for both tumor subtyping and survival prediction. For data details and pre-processing, please see Appendix A.4. BReAst Carcinoma Subtyping (BRACS) [4]... The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) [68, 55]... TCGA-COADREAD... TCGA-STAD... |
| Dataset Splits | Yes | For TCGA-BRCA, we perform 10-fold cross-validation with the same data split adopted in HIPT [9]. Besides, the dataset BRACS is officially split into training, validation and testing, thus the experiment is conducted 5-times with different random seeds. |
| Hardware Specification | Yes | We train our model with Py Torch on a RTX-3090 GPU |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We train our model with Py Torch on a RTX-3090 GPU, with a WSI-level batchsize of 1, learning rate of 1e-4, and weight decay of 1e-2. We add positional encoding into the framework, please check our code for details. |