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Lung tumor detection

As explained in Deep Learning with PyTorch, Second Edition

The task of automatically identifying suspected malignant lung tumors from chest CT scans.

Lung tumor detection automatically examines a patient’s chest CT scan and identifies the location of suspected malignant tumors, if any exist. CT scans are three-dimensional, single-channel arrays, making the task more demanding than ordinary image classification. The practical motivation is early detection: specialists must search large amounts of data, most of which contains no cancer, while maintaining painstaking attention to detail. section 11.1

From scan to suspected tumor

A three-dimensional CT scan of a human torso enters the system; the output identifies the location of a suspected malignant lung tumor or indicates that none is present. section 11.1

How Deep Learning with PyTorch, Second Edition covers it

How deep it goes
Defines the CT input and tumor-location output, but stops before explaining detection methods or implementation details.
Examples
Uses three-dimensional chest and torso CT scans, suspected tumor locations, and the predominance of scans without cancer.
What you can do after
Evaluate/understand the task’s input, output, medical motivation, and practical difficulty.
How it connects
Connects CT scans with lung cancer, malignant tumors, medical imaging, and early detection.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with PyTorch, Second Edition. The section links point to the parts of the book it draws on.