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Hi.
I'm trying to compare to some of the results in your work, but it's not clear to me which datasets were use for Table 1 and Table 2.
The Datasets A file contains 108 datasets, and the Datasets B…
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Hi. Thanks for sharing the wonderful work. In your paper (Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal…
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> Fig 7 investigates the SHAP values for an RF trained on the breast-cancer dataset. After applying HS, the SHAP values for each feature have tighter clusters. Each cluster comprises a group of sample…
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> Fig 7 investigates the SHAP values for an RF trained on the breast-cancer dataset. After applying HS, the SHAP values for each feature have tighter clusters. Each cluster comprises a group of sample…
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### Is your feature request related to a problem? Please describe.
Yes, In breast cancer prediction model I think we can also add a model to predict the stage of cancer from the tumor size for early …
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Чланови тима:
Даница Газдић SV12-2022, Милош Обрадовић SV55-2020 (Група 4)
Асистент:
Марко Његомир
Проблем:
Откривање малигних тумора дојке алгоритмима машинског учења.
Алгоритми:
Користи…
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Hey I would like to work on open source AutoML frameworks such as Auto-Sklearn, TPOT, H2O AutoML etc for breast cancer prediction to figure out the best model for this task with optimized hyper-parame…
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@bgruening @qiagu
To publish I suggest that we perform two analyses:
1. Reproduce the work in [this paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5890912/). This is a large analysis that …
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The outliers can be removed using the interpolation method.
Once it's removed, using the Deep Learning model - CNN, we can train the model and test it.
@SrijanShovit Please assign me this issue