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Labeling tool

As explained in Machine Learning System Design

A labeling tool is software that presents data and annotation controls for creating or reviewing machine-learning labels.

A labeling tool presents data and annotation controls for creating or reviewing machine-learning labels. Its design must match the label type, required expertise, time available, and product goal. section 6.2.4

Choose the tool and workflow around the required annotation:

  • Binary classification asks whether an image contains malignancy. section 6.2.4
  • Multiclass classification records which malignancy, if any, appears. section 6.2.4
  • Detection identifies the region containing malignancy; segmentation marks its exact pixels. section 6.2.4
  • Efficient operation also requires task decomposition, clear instructions, usable interfaces, quality control, aggregation, pricing, and process evaluation. section 6.2.4

Radiology images

A radiology team may label the same images with a binary, multiclass, detection, or segmentation interface. The choice changes labeling effort, team expertise, and the models and product the dataset can support. section 6.2.4

Figure 6.2 Various approaches to data labeling
figure

Compare the annotation formats and the increasing detail they require. section 6.2.4

How Machine Learning System Design covers it

How deep it goes
Connects interface choice to annotation detail, expertise, time, quality control, aggregation, and pricing.
Examples
Uses radiology images labeled for malignancy classification, region detection, or pixel-level segmentation.
What it assumes
Assumes product goals and dataset needs are understood; teaches how annotation choices constrain models and products.
What you can do after
Operate/configure labeling workflows by selecting annotation formats, interfaces, instructions, quality controls, and aggregation methods.
How it connects
Links data labeling with classification, detection, segmentation, weakly supervised learning, quality control, and aggregation.
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