# A Practical guide on Explainable AI Techniques applied on Biomedical use case applications

arXiv: https://arxiv.org/abs/2111.14260 · HTML: https://mlsanity.com/paper/2111.14260/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Grad-CAM: Why did you say that?](https://mlsanity.com/paper/1611.07450/) (2016)
- [Higher-Order Explanations of Graph Neural Networks via Relevant Walks](https://mlsanity.com/paper/2006.03589/) (2020)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [What Does Explainable AI Really Mean? A New Conceptualization of Perspectives](https://mlsanity.com/paper/1710.00794/) (2017)
- [Measurable Counterfactual Local Explanations for Any Classifier](https://mlsanity.com/paper/1908.03020/) (2019)
- [Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation](https://mlsanity.com/paper/1611.08191/) (2016)

## Built on by (most cited first, 3 of 3)

- [A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas](https://mlsanity.com/paper/2302.12594/) (2023)
- [Explainable AI for tool wear prediction in turning](https://mlsanity.com/paper/2308.08765/) (2023)
- [Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling](https://mlsanity.com/paper/2607.18515/) (2026)

## Abstract (arXiv)

Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments, the danger is to create and use decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing machine learning models with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is meant to be the go-to handbook for any audience with a computer science background aiming at getting intuitive insights on machine learning models, accompanied with straight, fast, and intuitive explanations out of the box. This article aims to fill the lack of compelling XAI guide by applying XAI techniques in their particular day-to-day models, datasets and use-cases. Figure 1 acts as a flowchart/map for the reader and should help him to find the ideal method to use according to his type of data. In each chapter, the reader will find a description of the proposed method as well as an example of use on a Biomedical application and a Python notebook. It can be easily modified in order to be applied to specific applications.