https://github.com/skent259/ordinal-mil-nnets
Experiments testing ordinal and multiple instance learning neural networks
https://github.com/skent259/ordinal-mil-nnets
multiple-instance-learning neural-network ordinal python reproducible-research simulation weakly-supervised-learning
Last synced: 10 months ago
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Experiments testing ordinal and multiple instance learning neural networks
- Host: GitHub
- URL: https://github.com/skent259/ordinal-mil-nnets
- Owner: skent259
- License: mit
- Created: 2022-07-13T01:37:20.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-08-31T03:38:51.000Z (almost 3 years ago)
- Last Synced: 2025-01-31T11:47:12.400Z (over 1 year ago)
- Topics: multiple-instance-learning, neural-network, ordinal, python, reproducible-research, simulation, weakly-supervised-learning
- Language: Python
- Homepage:
- Size: 30 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Ordinal, Multiple Instance Deep Learning
Many approaches have been proposed for deep learning for ordinal labels or multiple-instance learning (MIL) structure separately. However, few works have proposed a deep learning framework for ordinal labels and MIL structure together. This repository contains tensorflow-based code for implementing a deep learning framework for ordinal, MIL data, and includes experiments from the manuscript "Ordinal, Multiple Instance Deep Learning" by Sean Kent and Menggang Yu.
As a quick reference for getting started:
* The experiments can be run via the `run.sh` file, however this will take considerable CPU time. We recommend that you run in a high-throughput environment in batches (see `run.sh` for inspiration)
* Raw model code is contained in the `models/` directory, in addition to helper code that allows for running the experiment and application.
* If you are looking for a quick way to re-use the underlying code for your own deep learning, the examples in the `test/` directory are a useful place to start.
* Analysis, including code to replicate the figures and tables, is in the `analysis/` directory for the main experiment and the `application/` directory for an application to TMA data.
* Data (open-source) can be downloaded and processed in individual folders under the `datasets/` directory.
* The `condor/` directory can be ignored. It was used to run simulations in the HTCondor (high-throughput) environment.
## Methods compared
A full description of the methods is given in the manuscript. Where code was not available in a package, but the implementation was present elsewhere, we have copied the code into a directory with credit given below.
| Method name | Type | Directory | Reference |
| ------------------------ | ------- | --------------------------------------------------------- | ------------- |
| mi-net | MIL | `mil_nets/` | [1], [2], [3] |
| MI-net | MIL | `mil_nets/` | [2] |
| MI-net (DS) | MIL | `mil_nets/` | [2] |
| MI-net (Attention) | MIL | `mil_attention/` | [3] |
| MI-net (Gated-attention) | MIL | `mil_attention/` | [3] |
| CORAL | Ordinal | NA, see "coral-ordinal", "coral-pytorch" python packages | [4] |
| CORN | Ordinal | NA, see "coral-ordinal", "coral-pytorch" python packages | [5] |
| CLM QWK | Ordinal | `clm_qwk/` | [6] |
* `mil_nets/`: Originally used for inspiration/testing, but final code uses layers from tensorflow.
* `mil_attention/`:
* `clm_qwk/`
* "coral-ordinal":
* "coral-pytorch":
## References
[1] Ramon, J., & De Raedt, L. (2000). Multi instance neural networks. Proceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learning, 53–60.
[2] Wang, X., Yan, Y., Tang, P., Bai, X., & Liu, W. (2018). Revisiting multiple instance neural networks. Pattern Recognition, 74, 15–24. https://doi.org/10.1016/j.patcog.2017.08.026
[3] Ilse, M., Tomczak, J., & Welling, M. (2018). Attention-based deep multiple instance learning. Proceedings of the 35th International Conference on Machine Learning, 2127–2136. https://proceedings.mlr.press/v80/ilse18a.html
[4] Cao, W., Mirjalili, V., & Raschka, S. (2020). Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters, 140, 325–331. https://doi.org/10.1016/j.patrec.2020.11.008
[5] Shi, X., Cao, W., & Raschka, S. (2022). Deep neural networks for rank-consistent ordinal regression based on conditional probabilities. ArXiv Preprint ArXiv:2111.08851. http://arxiv.org/abs/2111.08851
[6] Vargas, V. M., Gutiérrez, P. A., & Hervás-Martínez, C. (2020). Cumulative link models for deep ordinal classification. Neurocomputing, 401, 48–58. https://doi.org/10.1016/j.neucom.2020.03.034