Relational Classification of Biological Cells in Microscopy Images
Authors: Ping Liu, Mustafa Bilgic344-352
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 pliu19@hawk.iit.edu, mbilgic@iit.edu |
| 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. |