Interpreting the loss functions of Artificial neural networks in cancer research

2023 · doi:10.32388/0drdvr
preprint OA: closed CC-BY-4.0
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This paper explores how different loss functions like MSE, RMSE, and Cross-Entropy are applied in cancer research using artificial neural networks to optimize model performance based on the research question and data type.

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⚙ AI-generated deep summary by claude@2026-07, 2026-07-14 · read from full text ⓘ

This paper provides a methodological examination of how artificial neural network loss functions are interpreted in the context of cancer research, focusing on the relationship between chosen loss functions and model behavior. It surveys or explains loss-function concepts rather than analyzing a specific clinical or biological dataset, and it frames the discussion around performance interpretation in cancer-related machine learning settings. The main limitation is that the work is not presented as an empirical, condition-specific study with outcomes from a defined patient population. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Artificial Neural Networks (ANNs) have become a popular tool in cancer research for their ability to learn complex relationships between input variables and clinical outcomes. One of the crucial components of ANNs is the loss function, It measures the difference between the output that was anticipated and the output that was produced. In cancer research, different loss functions are used depending on the nature of the research question and the type of data being analyzed. The optimal loss function is critical to ensure optimal performance of the ANN model. The Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used in regression tasks, while Cross-Entropy (CE) is often used in classification tasks. The optimal selection of loss function depends on the specific research question and data being analyzed.
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