# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 2 Background
### Measures for Explanations
#### 2.2.1 What Defines a High-Quality Explanation  
The question what defines a high-quality explanation is an important first question to consider. There are studies that attempt to explore this question [90; 91; 92]. One question that is important is the time taken to understand the explanation. Ideally, the explanation is thorough enough to convey the necessary information yet concise enough that is to be understood in a timely manner. The authors of [91] explore this by asking what makesexplanations explainable in the context of verification. The authors conduct an user study and identify what kind of increases in complexity have the greatest effect on the time it takes for humans to verify the rationale, and which seem relatively insensitive. Another factor to consider is the question who will be the end user of the algorithm. The end user of the deep learning system should guide the approach to evaluation. Designing explanation techniques for clinical settings require different explanations than designing explanation for robotics. A paper published in 2018 by [92] discusses the different roles involving explanation techniques and how the explanations differs depending on what role you are providing the explanation for. They show that an explanation for one role should be measured differently for other roles. This sounds trivial but is often ignored. For example, measuring explanations for doctors should have different criteria than measuring explanations for patients receiving the diagnosis or engineers designing the system. Consequentially, this suggests the need to test explanations on a variety of human subjects to evaluate for whom the explanations fail and for whom the explanation provides adequate information. For example, the explanation can be thorough and complete for a clinicians but fail for a patient when ideally it should address the needs of each end user. A limitation of the current literature is the lack of studies that verify explanation methods with a population of radiologists or doctors. To the best of our knowledge, there is not any studies that incorporate radiologists into the design process of deep learning explanation algorithms. The majority of human-studies involved general, non-specialized individuals recruited through a crowd-sourcing service such as Amazon Mechanical Turk [93]. The authors of [94] also highlight the importance of keeping the end-user in mind when defining what is explainable and how to measure it. It is also essential to have quantitative measures to be able to compare different methodologies quantitatively when qualitative measures fail. A paper published by the authors of [95] propose Remove and Retrain (ROAR) to quantitatively compute feature importance. ROAR ranks feature importance, removes these the features ranked as most important, and then retrains the model and measures the difference during testing. This method is widely used but there still lacks a standard procedure for quantitatively measuring explanation performance. For measuring qualitatively, [PERSON] and [PERSON] discuss factors related to qualitative assessment. First, what are explanations composed of? For example, feature importance or examples from the training? It other words, what units are being used to provide an explanation. Second, what are the number of units that explanations are composed of? If the explanation finds similar training examples to provide an explanation, does it just provide the most relevant examples or all? Third, if there is only a select few examples how are these ordered and what determines that criteria? If they are ranked in terms of similarity how is similarity defined? Or if they are ranked in terms of feature importance, how do you measure feature importance? Fourth, how do you show the interaction among these units in a human-digestible way? Fifth, how do you quantitatively show the uncertainty in an explanation?