Theo Di Piazza commited on
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README.md
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The CT-Scroll architecture consists of three main components. (1) Axial slices of the volume are grouped into triplets and processed by a ResNet followed by a GAP layer, producing a vector representation per triplet. (2) The Scrolling Block then refines these embedded visual tokens using both global and local attention mechanisms. (3) Finally, the aggregated representations are fed into a classification head to predict anomalies.
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<img src="./figures/method_overview.
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## Clone the Repository
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## Code repository
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The project source code can be found [here](https://github.com/theodpzz/ct-
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## Acknowledgments
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The CT-Scroll architecture consists of three main components. (1) Axial slices of the volume are grouped into triplets and processed by a ResNet followed by a GAP layer, producing a vector representation per triplet. (2) The Scrolling Block then refines these embedded visual tokens using both global and local attention mechanisms. (3) Finally, the aggregated representations are fed into a classification head to predict anomalies.
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<img src="./figures/method_overview.png" width="1000">
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## Clone the Repository
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## Code repository
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The project source code can be found [here](https://github.com/theodpzz/ct-scroll).
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## Acknowledgments
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