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highlights:2025:foramslice [2025/12/02 06:34] – Didier Barradas Bautistahighlights:2025:foramslice [2025/12/02 06:38] (current) – Didier Barradas Bautista
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 ===== Research Breakthrough ===== ===== Research Breakthrough =====
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-Foraminifera are microscopic organisms whose fossilized shells reveal the history of Earth's climate. Our **ForamDeepSlice** framework uses deep learning to automatically classify 12 foraminifera species from micro-CT scans with unprecedented accuracy.+Foraminifera are microscopic organisms whose fossilized shells provide insight into the history of Earth's climate. Our **ForamDeepSlice** framework uses deep learning to automatically classify 12 foraminifera species from micro-CT scans with unprecedented accuracy.
   * 📊 Tested on 97 specimens representing 27 species, ForamDeepSlice achieved **95.6%** accuracy and **99.6%** top-3 accuracy across 109,617 2D slices.   * 📊 Tested on 97 specimens representing 27 species, ForamDeepSlice achieved **95.6%** accuracy and **99.6%** top-3 accuracy across 109,617 2D slices.
   * 🎯 The method includes an interactive dashboard for real-time classification and 3D slice matching.   * 🎯 The method includes an interactive dashboard for real-time classification and 3D slice matching.
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 <fs 18>**🔬 Research Visualization**</fs> <fs 18>**🔬 Research Visualization**</fs>
-The workflow diagram below illustrates our comprehensive deep learning pipeline for automated foraminifera classification. The paper entitled "ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices" is available for download and review [[https://arxiv.org/|here]]. +The workflow diagram below illustrates our comprehensive deep learning pipeline for automated foraminifera classification. The paper entitled "ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices" is available for download and review [[https://arxiv.org/abs/2512.00912|here]]. 
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-{{:wiki:highlights:2025:foram_pipeline.png?1000}}+{{:wiki:highlights:2025:foramdeepslice_workflow_v1.jpg?1000}}
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 <fs 18>**🎯 KVL's Contribution**</fs> <fs 18>**🎯 KVL's Contribution**</fs>
-<color #000000>KVL's visualization scientists **Abdelghafour Halimi**, **Didier Barradas-Bautista**, and **Ronell Sicat** contributed significantly to the development of the ForamDeepSlice framework through:+KVL's visualization scientists **Abdelghafour Halimi**, **Didier Barradas-Bautista**, and **Ronell Sicat** contributed significantly to the development of the ForamDeepSlice framework through:
  
   * Design and implementation of PatchEnsemble strategy for improved classification accuracy   * Design and implementation of PatchEnsemble strategy for improved classification accuracy
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   * Optimization of preprocessing pipeline and data augmentation strategies   * Optimization of preprocessing pipeline and data augmentation strategies
   * Public release of the ForamDeepSlice framework for scientific community use   * Public release of the ForamDeepSlice framework for scientific community use
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