# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 4 Discussion  
There has been significant research progress with regards to explainable deep learning cancer detection systems. The majority of methods utilized in the literature are local, post-hoc methods that are usually model and data specific. Current research demonstrate a trajectory heading towards explainable deep learning for cancer diagnosis, but there is still work to be done to fully realize the implementation of these systems in a clinical setting. When a methodology provides an obviously wrong explanation it is important to be able to probe more to find out what went wrong. There is little research around this area despite being important for clinical implementation. This would help alleviate misclassification errors and provide greater insight to the clinician. Being able to quantify uncertainity in explanations is important for clinicians as well. This would give the clinician \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Condition** & **Explanation Type** & **Method** & **Evaluation Type 1 ** \\ \hline [PERSON] et al., 2018 [136] & Breast Cancer & Ad-Hoc & Deep-Miner & PE \\ [PERSON] et al., 2019 [135] & Lung Cancer & Ad-Hoc & explainable Deep Hierarchical Semantic CNN & PE \\ [PERSON] et al., 2019 [134] & Lung Cancer & Ad-Hoc & SICS & NS \\ [PERSON] et al., 2018 [137] & Breast Cancer & Post-Hoc & Grad-CAM & NS \\ [PERSON] et al., 2018 [29] & Brain Cancer & Post-Hoc & Pyramid Grad-CAM & PE \\ [PERSON] et al., 2020 [100] & Brain Cancer & Post-Hoc & Grad-CAM & HE \\ [PERSON] et al., 2017 [127] & Prostate Cancer & Post-Hoc & CAM & NS \\ [PERSON] et al., 2020 [32] & Prostate Cancer & Post-Hoc & Grad-CAM and Saliency Maps & PE \\ [PERSON] et al., 2020 [128] & Lung Cancer & Post-Hoc & Grad-CAM & NS \\ [PERSON] et al., 2020 [138] & Liver Cancer & Post-Hoc & Saliency Maps & NS \\ [PERSON] et al., 2019 [101] & Liver Cancer & Post-Hoc & CAM & HE \\ [PERSON] et al., 2020 [129] & Breast Cancer & Post-Hoc & Integrated Gradients & HE \\ [PERSON] et al., 2019 [132] & Brain Cancer & Ad-Hoc & Capsule Network & HE \\ [PERSON] et al., 2020 [133] & Lung Cancer & Ad-Hoc & X-Caps & HE \\ [PERSON] et al., 2020 [131] & Brain Cancer & Ad-Hoc & explainable Model & CS \\ [PERSON] et al., 2019 [130] & Lung Cancer & Ad-Hoc & Supervised Iterative Descent & PE \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Explanation Techniques for Deep Learning Cancer Detection. a gauge for the amount of trust to have in an explanation. This would also help alleviate potential errors introduce by explanation techniques. Since the end user of explainability approaches for cancer detection will be clinicians and their patients, it is essential to take into account what they consider to be explainable. This is an important aspect that is often ignored by explanation studies. Most algorithms are measured using quantitative methods such as log loss or human studies of non-clinicians recruited from a service such as Amazon Mechanical Turk. There is a need to incorporate clinicians into the design process of these algorithms and to evaluate the algorithms with the doctors that will be using them. There are studies that attempt to design clinical visualization development and evaluation with clinicians and researchers. The authors of [139] introduce Clinical-Vis which is an EHR visualization based prototype system for task-focused design evaluation of interactions between healthcare providers (HCPs) and EHRs. They design a study where they recruit 14 clinicians. They compare the clinicians interaction with their systems against a baseline system. They qualitatively analyzed the differences between systems by having the clinicians think out loud during the assigned tasks. The authors also conducted a survey after the task was complete and analyzed the surveys. The measured time taken to arrive at a decision, accuracy of decision, confidence in decision, and TLX scores which the authors define as \"Self-reported mental demand, physical demand, effort level, hurriedness, success and discouragement on a scale of 0-10\".