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Link to original content: https://doi.org/10.1007/978-3-031-19803-8_40
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention–Guided Disease Classification | SpringerLink
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RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention–Guided Disease Classification

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

In this work, we present RadioTransformer, a novel student-teacher transformer framework, that leverages radiologists’ gaze patterns and models their visuo-cognitive behavior for disease diagnosis on chest radiographs. Domain experts, such as radiologists, rely on visual information for medical image interpretation. On the other hand, deep neural networks have demonstrated significant promise in similar tasks even where visual interpretation is challenging. Eye-gaze tracking has been used to capture the viewing behavior of domain experts, lending insights into the complexity of visual search. However, deep learning frameworks, even those that rely on attention mechanisms, do not leverage this rich domain information for diagnostic purposes. RadioTransformerfills this critical gap by learning from radiologists’ visual search patterns, encoded as ‘human visual attention regions’ in a cascaded global-focal transformer framework. The overall ‘global’ image characteristics and the more detailed ‘local’ features are captured by the proposed global and focal modules, respectively. We experimentally validate the efficacy of RadioTransformeron 8 datasets involving different disease classification tasks where eye-gaze data is not available during the inference phase. Code: https://github.com/bmi-imaginelab/radiotransformer

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Acknowledgement

The reported research was partly supported by NIH 1R21CA258493-01A1, NIH 75N92020D00021 (subcontract), and the OVPR and IEDM seed grants at Stony Brook University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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  1. Stony Brook University, Stony Brook, New York, USA

    Moinak Bhattacharya, Shubham Jain & Prateek Prasanna

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  1. Moinak Bhattacharya
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  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Bhattacharya, M., Jain, S., Prasanna, P. (2022). RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention–Guided Disease Classification. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13681. Springer, Cham. https://doi.org/10.1007/978-3-031-19803-8_40

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