# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
How explanability brings value to the end user is depicted in Figure 3.  
Figure 1: This figure shows the number of published papers using deep learning models to predict cancer risk, classification, prediction and detection per year since 2008. The data were collected using keyword searches through PubMed. Explainable and interpretable are used interchangeably in parts of the literature [33] yet they are defined separately in others. This work will use the terms are used interchangeably. There is not an agreed upon definition of explainability. As [PERSON] mentioned, \"the term explainability holds no agreed upon meaning, and yet machine learning conferences frequently publish papers which yield the term in a quasi-mathematical way\". This demonstrates the ambiguity of term and the need for a formal definition. That is outside the scope of this work but for the context of this paper, we adopt the [34] definition of explainability which is, \"explainability is the degree to which a human can understand the cause of a decision\". We conducted an extensive literature review by examining relevant papers from six major academic databases. A keyword based search was used to select papers, it consists of searching for index keywords based on common variations in literature of the terms \"explainable\", \"transparency\", \"black box\", \"understandable\", \"comprehensible\", and \"explainable\" and its union with a set of terms related to deep learning cancer detection including \"deep learning\", \"machine learning\" and \"convolutional neural network\". The research was restricted to articles published between 2012 and 2020 with one exception published in 1991. The papers were skimmed and then filtered by quality and relevancy. More papers were then added by using a snowballing strategy that consists of using the reference list of the selected papers to identify additional papers. The contributions of this review are three fold. First, this work will focus on explainability for cancer detection using MRI images. Second, we provide a thorough overview of the current approaches to measure explainability that are commonly used in the literature and why this is important for medical explainability. There is not an universally agreed upon metric for an explanation. This review will discuss the advantages and disadvantages of metrics commonly found in the literature. Third, this review will discuss papers related to what clinicians consider important for explanation systems. This highlights a  
Figure 3: How explanation methods provide value to the end user. Figure 2: In general, more accurate models are less explainable. gap between ML researchers who design the algorithms and clinicians who are the end users. This paper attempts to discuss direction for closing that gap. This paper is organized in the following way. First, a general taxonomy of explanation approaches for deep learning cancer detection systems is described. Second, current measures for explanation quality are examined and analyzed. Third, we discuss explanation techniques applied to cancer detection deep learning models. Finally, it will conclude with a discussion with suggested future directions in section four and five respectively.