Interaction with autonomous agents for medical decision support

The aim of the research is to explore the interaction between humans and computers by the design of contexts that create efficient ensembles of medical practitioners and learning machines.

The last decade's advancements in machine learning (ML) has led to a dramatic increase in AI capabilities and the viability of learning by example. However, despite impressive technical advances and many successful research projects, machine algorithms for medical diagnostics are to a very small extent used in healthcare today.

One challenge is that for ML algorithms with less than 100% sensitivity and specificity the clinical user needs effective means to assess the validity of results and incorporate this knowledge within the broader context of their diagnostic process.

The research explorations involve viewing this interaction as a process that unfolds over time enabling reciprocal and continuous learning as well as framing machine learning as material in the design process and investigating the limits, extent and characteristic of the design space that this new material affords.

Tool for training a machine learning system
Tool for training a machine learning system to classify tissues. The design emphasizes rapid refinement by near-realtime feedback and enables human-machine synergies by improving by continuously improving with use. Martin Lindvall
Diagnostic prototype for tumor area assessment
Diagnostic prototype for tumor area assessment. Predictions are presented in an input/output-symmetric way that enables direct manipulation of algorithmic predictions and the continous training of the underlying prediction model. Martin Lindvall
interative tool in assissting lymphnode adenocarcinomas
Iterative refinement of annotation strategy while developing a interative tool in assissting lymphnode adenocarcinomas. The medical domain expert uses the output of the preliminary model to assess whether their annotation is wrong or whether the prediction is wrong. Martin Lindvall

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External partners

Sectra AB, Region Östergötland, Region Gävleborg, University of Leeds

WASP research at MITShow/Hide content

Efficient data representation for visual machine learning

Image synthesis and efficient data representations for visual machine learning

Driven by the high accuracy and performance of deep learning algorithms in computer vision tasks, we are investigating the use and production of highly realistic training data-sets for computer vision applications.

Visualization for understanding and developing machine learning

Machine learning is deployed in a wide range of application domains. As with most large-scale data driven approaches it is hard to analyze and understand what is the underlying learned model, how accurate is it, and exactly how it is solved.
Visualization of tissues

Orchestrating machine learning development in complex medical imaging environments

Even though machine learning algorithms have shown promising results in many medical image analysis tasks there are still a lot of challenges that need to be solved before these advances can be used in clinical practice.

visual methods to guide and focus the user’s attention

Focal/peripheral data visualization for industrial control rooms

The amount of operation and maintenance data delivered to the industrial control room operator grows manifold. In the scope of a PhD project, we are working with the problem of information overload.

Automatic pattern tracking and visualization for decision support

Monitoring autonomous systems plays a key role in understanding the systems’ functionality and in ensuring their safety. Without appropriate data reduction and visual support this is hardly possible.

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