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..
Relational Classification of Biological Cells in Microscopy Images
Authors: Ping Liu, Mustafa Bilgic344-352
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real datasets show that R-LSTM performs comparable to or better than six baselines. |
| Researcher Affiliation | Academia | Ping Liu and Mustafa Bilgic Illinois Institute of Technology, Chicago, IL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Synthetic data generation; Algorithm 2 Stratified fold generation |
| Open Source Code | Yes | The code to replicate the experimental results in this paper is available at https://github.com/IITML/AAAI21-relational-cell-classification. |
| Open Datasets | Yes | Histology Images of Colorectal Cancer (CRC) This dataset is introduced by Sirinukunwattana et al. (2016)... Human MCF7 Breast Cancer Cells (MCF-7) MCF-7 is a public image set that was collected and labeled... by Broad Bioimage Benchmark Collection2. ... (Piccinini et al. (2017); Toth et al. (2018))... Urinary Bladder Cancer Tissue Sections (UBC) UBC image dataset (Toth et al. 2018) is a collection of microscopy images... Footnote 2: https://data.broadinstitute.org/bbbc/BBBC021/ |
| Dataset Splits | Yes | We perform 10-fold stratified cross validation, where eight folds are used for training, one fold for validation, and one fold for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | For the deep learning models (AE, CNN, LCNN, RLSTM) we used Py Torch (Paszke et al. 2017). For SVM, we used scikit-learn (Pedregosa et al. 2011). The text does not specify version numbers for PyTorch or scikit-learn. |
| Experiment Setup | No | The paper describes the model architectures, datasets, and cross-validation strategy, but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) for the deep learning models. |