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Cross-Modal Machine Learning as a Way to Prevent Improper Pathology Diagnostics

Sophie Turol

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Machine learning techniques are widely employed to aid doctors in diagnosing. Still, enabling precise image recognition and analysis can be a real painful experience. This blog post explores why it happens and seeks the ways out as discussed at TensorBeat 2017.

 

Why is analysis of medical images difficult?

Dave Singhal talked about the challenges of applying machine learning to improve diagnostics. In particular, identifying good vs. bad pathologies to prevent unnecessary removal of an organ or dealing with pathologies that spread to different organs.

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Image recognition in healthcare is anything but an easy task with indistinct edges, numerous tiny elements, low contrast, etc. In addition to that, medical data comes in multiple forms—MRI, sonograms, X-rays, etc.—with different modalities.

However, the spectrum of machine learning tools is diverse enough to address such tasks as:

  • classification
  • detection
  • segmentation
  • registration
  • text + modality
  • cross-modality
  • image retrieval, generation, and enhancement

Dave is enthusiastic about exploring the capabilities of what he refers to as cross-modal learning, which is “one model helping the other to do the analysis.” Dave exemplified the U-net architecture of a convolutional neural network that ensures fast and precise image segmentation.

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He also demonstrated what a shared cross-modal representation, which is a set of modal-specific convolutional neural networks, looks like.

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However, using such an approach for medical images is challenging as they hardly have sharp features, shapes are mostly not generalizable, edges are gradual, etc.

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Dave concluded with an idea of using machine learning for:

  • cross-segmentation, -representation, -retrieval, and -analysis
  • cross-visual attention maps
  • surfacing sub-perceptual (sub-threshold) features

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So, machine learning is evolving to save even more lives through helping doctors to avoid mistakes when diagnosing pathologies of different complexities, while automating routine tasks.

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About the speaker

Dave Singhal is a scholar at Stanford University. He is exploring neuro-inspired machine learning approaches on multi-modal image data sets and evaluating hardware and software constructs to improve machine learning efficiency. With 10+ patents under his belt, Dave is also a principal at Light Field Interactive—dealing with imaging, graphics, optics, and perception. His aim is to advance this area through academic program development, industry collaboration events, and multi-site research projects. Prior to this, Dave served as Director of Engineering at Cisco Systems.


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