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highlights:2025:foramslice [2025/12/02 06:33] – Didier Barradas Bautistahighlights:2025:foramslice [2025/12/02 06:38] (current) – Didier Barradas Bautista
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 {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Abdelghafour Halimi \\ {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Abdelghafour Halimi \\
 {{:icon:twbs:link:w-25:h-auto:envelope-at.svg?nolink&}} abdelghafour.halimi@kaust.edu.sa \\ {{:icon:twbs:link:w-25:h-auto:envelope-at.svg?nolink&}} abdelghafour.halimi@kaust.edu.sa \\
-{{:icon:twbs:link:w-25:h-auto:building.svg?nolink&}} Building 1, Level 0, Office 0125 \\ 
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-<WRAP center round box 100%> {{:wiki:kaust-seeds.png?nolink&0x30}} <fs:26px>** Collaborators**</fs> 
  
 {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Didier Barradas-Bautista \\ {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Didier Barradas-Bautista \\
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 {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Ronell Sicat \\ {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Ronell Sicat \\
 {{:icon:twbs:link:w-25:h-auto:envelope-at.svg?nolink&}} ronell.sicat@kaust.edu.sa \\ {{:icon:twbs:link:w-25:h-auto:envelope-at.svg?nolink&}} ronell.sicat@kaust.edu.sa \\
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 +{{:icon:twbs:link:w-25:h-auto:building.svg?nolink&}} Building 1, Level 0, Office 0125 \\
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 +<WRAP center round box 100%> {{:wiki:kaust-seeds.png?nolink&0x30}} <fs:26px>** Collaborators**</fs>
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 {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Ali Alibrahim \\ {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Ali Alibrahim \\
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 ===== Research Breakthrough ===== ===== Research Breakthrough =====
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-Foraminifera are microscopic organisms whose fossilized shells reveal Earth's climate history. 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
-  * Curation of comprehensive micro-CT foram dataset with rigorous specimen-level splitting+  * Curation of a comprehensive micro-CT foram dataset with rigorous specimen-level splitting
   * Evaluation and benchmarking of 7 state-of-the-art CNN architectures   * Evaluation and benchmarking of 7 state-of-the-art CNN architectures
-  * Development of interactive dashboard with real-time classification and 3D slice matching+  * Development of an interactive dashboard with real-time classification and 3D slice matching
   * 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
-</color>+
  
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