Abstract
Although deep learning-based AI systems for diagnostic imaging tasks have virtually showed superhuman accuracy, their use in medical settings has been questioned due to their “black box”, not interpretable nature. To address this shortcoming, several methods have been proposed to make AI eXplainable (XAI), including Pixel Attribution Methods; however, it is still unclear whether these methods are actually effective in “opening” the black-box and improving diagnosis, particularly in tasks where pathological conditions are difficult to detect. In this study, we focus on the detection of thoraco-lumbar fractures from X-rays with the goal of assessing the impact of PAMs on diagnostic decision making by addressing two separate research questions: first, whether activation maps (as an instance of PAM) were perceived as useful in the aforementioned task; and, second, whether maps were also capable to reduce the diagnostic error rate. We show that, even though AMs were not considered significantly useful by physicians, the image readers found high value in the maps in relation to other perceptual dimensions (i.e., pertinency, coherence) and, most importantly, their accuracy significantly improved when given XAI support in a pilot study involving 7 doctors in the interpretation of a small, but carefully chosen, set of images.
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Notes
- 1.
It is worth of note that activation maps are different from saliency maps, although the two terms are often used interchangeably. In fact, the two approaches rely on different methods to compute heatmaps: saliency maps are usually generated by means of back-propagation w.r.t. to the input of the network [35], while activation maps are obtained by means of the feature maps obtained at a specific layer of the network [42].
- 2.
More precisely, both specialists had to convene that the images were at least of level 3 (“sufficient image quality: moderate limitations for clinical use but no substantial loss of information”) on the absolute Visual Grading Analysis (VGA) scale [28], which is a 5-value ordinal scale from 1 (“excellent image quality: no limitations for clinical use”) to 5 (“poor image quality: image not usable, loss of information, image must be repeated”).
- 3.
Balance in model accuracy was also guaranteed at class level: in each group (fractured vs non-fractured), 2 images were associated with a misdiagnosis by the model while 4 were correctly classified.
- 4.
- 5.
- 6.
Clarity would be a fourth relevant dimension of heatmaps for radiological use or XAI settings, as it relates to the accurate presentation of anatomical or pathological structures. However, this dimension was not assessed by the sample of readers involved, as images had already been selected to be of optimal clarity. Moreover, correlation between clarity and utility has been conjectured to be obvious and not worthy of investigation.
- 7.
It is noteworthy that the white-box paradox can also mislead doctors when the advice is wrong, in that it can convince them of the opposite, as it has been reported in [7].
- 8.
It should be noted that “the implicit assumption [...] that the specific (diagnostic) message of the X-ray images resided inside them from the beginning, and that it is obscured either by technological or epistemological problems [is contestable as too naive]”. Conversely it has been argued [33] that “the specific content of the images was shaped by the activities of X-ray workers within the context of medical developments of the time” when x-ray imaging was introduced in medical practice at the beginning of the 20th century. In these days, we could be witnessing the same phenomenon, in which radiologists, specialists, data scientists and ML developers could participatorily co-develop a machine semiotics for specific diagnostic tasks, if they are willing.
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Cabitza, F., Campagner, A., Famiglini, L., Gallazzi, E., La Maida, G.A. (2022). Color Shadows (Part I): Exploratory Usability Evaluation of Activation Maps in Radiological Machine Learning. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2022. Lecture Notes in Computer Science, vol 13480. Springer, Cham. https://doi.org/10.1007/978-3-031-14463-9_3
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