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--- |
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inference: false |
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co2_eq_emissions: |
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emissions: 7540 |
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source: MLCo2 Machine Learning Impact calculator |
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geographical_location: East USA |
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hardware_used: Tesla V100-SXM2 GPU |
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tags: |
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- segmentation |
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license: gpl-3.0 |
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language: en |
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model-index: |
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- name: SpecLab |
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results: [] |
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--- |
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# SpecLab Model Card |
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This model card focuses on the model associated with the SpecLab space on Hugging Face, available [here](https://huggingface.co/spaces/Nano1337/SpecLab). |
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## Model Details |
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* **Developed by:** Haoli Yin |
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* **Model type:** Atrous Spatial Pyramid Pooling (ASPP) model for Specular Reflection Segmentation in Endoscopic Images |
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* **Language(s):** English |
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* **License:** GPL 3.0 |
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* **Model Description:** This is a model that can be used to create dense pixel-wise segmentation masks of detected specular reflections from an endoscopy image. |
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* **Cite as:** |
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```bib text |
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@misc{Yin_SpecLab_2022, |
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author = {Yin, Haoli}, |
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doi = {TBD}, |
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month = {8}, |
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title = {SpecLab}, |
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url = {https://github.com/Nano1337/SpecLab}, |
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year = {2022} |
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} |
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``` |
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## Uses |
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### Direct Use |
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The model is intended to be used to generate dense pixel-wise segmentation maps of specular reflection regions found in endoscopy images. Intended uses exclude those described in the [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use) section. |
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### Downstream Use |
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The model could also be used for downstream use cases, including further research efforts, such as detecting specular reflection in other real-world scenarios. This application would require fine-tuning the model with domain-specific datasets. |
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## Limitations and Bias |
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### Limitations |
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The performance of the model may degrade when applied on non-biological tissue images. There may also be edge cases causing the model to fail to detect specular reflection, especially if the specular reflection present is a different color than white. |
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### Bias |
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The model is trained on endoscopy video data, so it has a bias towards detecting specular reflection better on biological tissue backgrounds. |
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### Limitations and Bias Recommendations |
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* Users (both direct and downstream) should be made aware of the biases and limitations. |
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* Further work on this model should include methods for balanced representations of different types of specular reflections. |
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## Training |
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### Training Data |
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The GLENDA "no pathology" dataset was used to train the model: |
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* [GLENDA Dataset](http://ftp.itec.aau.at/datasets/GLENDA/), which contains ~12k image frames. |
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* Masks (to be released), were generated using the specular reflection detection pipeline found in this paper (to be released). |
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* Train/Val/Test was split randomly based on a 60/20/20 distribution. |
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### Training and Evaluation Procedure & Results |
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You can view the training logs [here at Weights and Biases](https://wandb.ai/nano-1337/Predict/reports/SpecLab-Training-for-10-Epochs--VmlldzoyNDYyNDIz?accessToken=xfjtfgb5szvsk08luvmwinjl6y2kvp1vl1eax52kbxgwgbwjqv29yed9elzgbju1) |
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During training, input images pass through the system as follows: |
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* Images are transformed by albumentations with horizontal/vertical flips to augment the data, normalized to [0, 1], and converted to a tensor. |
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* A forward pass is run through the model and the logits are output |
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* Loss is the "Binary Cross Entropy with Logits Loss" between the model prediction logits and the ground truth masks |
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* The logits are run through a sigmoid activation function and a threshold at 0.5 is set to binarize the output. |
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The simplified training procedure for SpecLab is as follows: |
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* **Hardware:** One 16GB NVIDIA Tesla V100-SXM2 |
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* **Optimizer:** Adam |
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* **Batch:** 4 samples |
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* **Learning rate:** initialized at 0.001 then CosineAnnealingLR with a T_max of 20. |
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* **Epochs:** 10 epochs |
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* **Steps:** 18k |
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## Environmental Impact |
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### SpecLab Estimated Emissions |
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Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. |
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* **Hardware Type:** Tesla V100-SXM2 |
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* **Hours used:** 6 |
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* **Cloud Provider:** Google Colab |
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* **Compute Region:** us-south1 |
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* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 0.7146 kg CO2 eq. |
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## Citation |
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```bibtext |
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@misc{Yin_SpecLab_2022, |
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author = {Yin, Haoli}, |
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doi = {TBD}, |
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month = {8}, |
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title = {SpecLab}, |
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url = {https://github.com/Nano1337/SpecLab}, |
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year = {2022} |
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} |
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``` |
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*This model card was written by: Haoli Yin* |
