if the cell is positive for this protein
and appears blue from the background
staining if it is not.
In the design of an ML pipeline, two
apparent choices of class labels for this
problem exist: pixel labels and nuclei
labels placed on the center of the nuclei.
If pixel labels are used, pixels belonging
to positive and negative nuclei can be
visualized to the user as an overlay on
top of the original image, occluding
the nuclei. If nuclei labels are used,
the result can be visualized by placing
glyphs on the center of each detected
nucleus. Using glyphs makes it easier for
the user to detect errors, since less ink
is used to visualize the result and the
original image becomes more visible. It
also becomes easier to correct misplaced
markers, since less precision is needed
to click on markers than on pixels. The
second approach was implemented as a
product, shown in Figure 5.
For the user to be able to correct the
results, in addition to actually seeing
the underlying phenomena they must
also be able to perform the validation
with reasonable effort in relation to
perceived gain. For this design, we
borrowed an heuristic from the light
microscope and limited predictions
to only 200 nuclei in the most prolific
region.
This design also illustrates
the seminal principle of direct
manipulation [ 3]; the result is
presented in an input-output symmetric
way in which the user can directly
manipulate the labeled data. By
designing the system to allow for
such direct manipulation, making the
validation possible with reasonable
effort and providing an intrinsic
incentive in terms of the actual nuclei
count, user corrections can be used
to directly retrain and improve the
underlying machine-learning model.
Our final example of an ML direct-manipulation interface is the patch
gallery prototype shown in Figure
6, where the goal is to estimate the
distribution of classes in an area. In
this prototype, we generate a grid
pattern over a user-selected area and
extract a small image patch for each
point in the grid. We then feed each
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.
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Figure 5. An example of a symmetric input-output ML-system of a cell-counting system
for KI- 67 stainings.