patch to a trained ML component that classifies the patches into different categories, which are then shown in a sorted gallery. Each defined class in the trained model is shown as a patch in the same gallery, and the user can then 1) click on a patch to see it in the main view to get a sense of its context in the tissue, and 2) change a label by either dragging the patch to the correct category or by clicking on the button or the corresponding shortcut key. Note that, similar to the previous example, the output of the classifier is limited to producing predictions not for the entire area, but only for a representative systematic sampling of that area, which makes the validation and correction effort tractable.

Like the mitotic counter in the previous section, both systems share the property that the generated parameter that the clinician wants to assess can be derived from manual input only. If the nuclei-detection algorithm failed to detect any nuclei, the user could still manually click on all the nuclei to calculate the KI- 67 index. However, the amount of clicking would likely overwhelm the user. These user-correction systems do not strictly need an ML component, but practical usability requires automated support with a certain level of prediction accuracy.

Another crucial factor when
designing this type of system is that the
user-correction accuracy needs to be
higher than that of the ML component
alone, in order for the generated
training data to add value when
retraining the ML component.

DISCUSSION

In the design of these tools, we have paid special attention to ensuring that manual, unassisted workflows are preserved and compatible with the assisting tools. When possible, training-data collection has been designed to become an integrated part of clinicians’ daily diagnostic practice. Furthermore, as the predictive performance of our initial ML components improve using the clinicians’ corrections as training data, we look forward to the point when our user interfaces need to be redesigned or augmented with interactions that are adapted to ML components with much higher performance. It is our ambition to design the new interfaces so that the user can provide corrections and simultaneously teach and verify results at increasingly higher levels, forming a verification staircase [ 4], as opposed to a steep cliff where the user has to validate all or nothing.

Looking forward, as the technology comes of age, we hope this human-centered teaching aspect will continue to spark novel designs as interactions expand from dyadic teacher-learners to networks of machine intelligences

DOI: 10.1145/3282860 COP YRIGHT HELD BY AUTHORS

and groups of human specialists co-evolving, teaching, and learning in daily practice.

Observing ML-based product development from the view of training-data generation, we have shown how decisions made by the UX designer have an enormous impact on project success. Each step of training-data generation needs to get the motivations right so that users are willing and able to provide corrections. The choice of what the training dataset should consist of and thus what the ML component should predict is tightly connected to how the user interface should look, behave, and be interacted with. Hence, in the creation of human-centered machine-learning systems, the UX designer plays a key role from the start and throughout.

 

Endnotes

1. McGuinness, K. and O’Connor, N.E. A comparative evaluation of interactive segmentation algorithms. Pattern Recognition 43, 2 (2010), 434–444.

2. von Ahn, L. and Dabbish, L.. Labeling images with a computer game. Proc. of the SIGCHI Conference on Human Factors in Computing Systems. ACM. New York, 2004, 319–326.

3. Shneiderman, B. The future of interactive systems and the emergence of direct manipulation. Behaviour & Information Technology 1, 3 (1982), 237–256.

4. Molin, J., Woźniak, P. W., Lundström, C., Treanor, D., and Fjeld, M. Understanding design for automated image analysis in digital pathology. Proc. of the 9th Nordic Conference on Human-Computer Interaction. ACM, New York, 2016, Article 58.

 

Martin Lindvall is an industrial Ph.D. student exploring interactions using machine learning as a design material. Currently part of the Wallenberg AI, Autonomous Systems and Software Program ( WASP), his background includes an M.Sc. in cognitive science and 10 years of experience designing and developing medical information systems at Sectra. → [e-mail]

Jesper Molin is research scientist and UX designer at Sectra, exploring and designing ML-based tools used within clinical routine pathology. His background includes an M. Sc. in applied physics and electrical engineering and a Ph.D. in human-computer interaction from Chalmers University of Technology. → [e-mail]

Jonas Löwgren is professor of interaction and information design at Linköping University, Sweden. His expertise includes collaborative media, interactive visualization, and the design theory of digital materials. → [e-mail]

Figure 6. Patch gallery prototype, samples from the tissue are generated and classified by an ML algorithm into three classes, which the user can correct by drag ’n drop.