Deep Learning with PyTorch by Eli Stevens Luca Antiga & Thomas Viehmann

Author:Eli Stevens, Luca Antiga & Thomas Viehmann [Eli Stevens, Luca Antiga & Viehmann, Thomas]
Language: eng
Format: epub
Publisher: Manning Publications Co.
Published: 0101-01-01T00:00:00+00:00

Figure 9.5 A CT scan with approximately 1,000 structures that look like tumors to the untrained eye. Exactly one has been identified as a nodule when reviewed by a human specialist. The rest are normal anatomical structures like blood vessels, lesions, and other non-problematic lumps.

You might have seen elsewhere that end-to-end approaches for detection and classi-fication of objects are very successful in general vision tasks. TorchVision includes end-to-end models like Fast R-CNN/Mask R-CNN, but these are typically trained on hundreds of thousands of images, and those datasets aren’t constrained by the number of samples from rare classes. The project architecture we will use has the benefit of working well with a more modest amount of data. So while it’s certainly theoretically possible to just throw an arbitrarily large amount of data at a neural network until it learns the specifics of the proverbial lost needle, as well as how to ignore the hay, it’s going to be practically prohibitive to collect enough data and wait for a long enough time to train the network properly. That won’t be the best approach since the results are poor, and most readers won’t have access to the compute resources to pull it off at all.

To come up with the best solution, we could investigate proven model designs that can better integrate data in an end-to-end manner.3 These complicated designs are capable of producing high-quality results, but they’re not the best because understanding the design decisions behind them requires having mastered fundamental concepts first. That makes these advanced models poor candidates to use while teaching those same fundamentals!

That’s not to say that our multistep design is the best approach, either, but that’s because “best” is only relative to the criteria we chose to evaluate approaches. There are many “best” approaches, just as there are many goals we could have in mind as we work on a project. Our self-contained, multistep approach has some disadvantages as well.

Recall the GAN game from chapter 2. There, we had two networks cooperating to produce convincing forgeries of old master artists. The artist would produce a candidate work, and the scholar would critique it, giving the artist feedback on how to improve. Put in technical terms, the structure of the model allowed gradients to backpropagate from the final classifier (fake or real) to the earliest parts of the project (the artist).

Our approach for solving the problem won’t use end-to-end gradient backpropagation to directly optimize for our end goal. Instead, we’ll optimize discrete chunks of the problem individually, since our segmentation model and classification model won’t be trained in tandem with each other. That might limit the top-end effectiveness of our solution, but we feel that this will make for a much better learning experience.

We feel that being able to focus on a single step at a time allows us to zoom in and concentrate on the smaller number of new skills we’re learning. Each of our two models will be focused on performing exactly one task. Similar to a

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