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README.md

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-## Repository Layout
+## Repository Layout
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+🚀 PointMIL is an ICCV 2025 Highlight — powering interpretable 3D cell shape analysis at Sentinal4D
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+At Sentinal4D, we’re pioneering the analysis of 3D cell shapes, and we believe models should be trustworthy by design.
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+PointMIL is our unified Multiple Instance Learning (MIL) framework that delivers state-of-the-art accuracy across 3D biomedical datasets and inherent interpretability.
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+Why this matters for biology
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+- Mapping drug effects in 3D: See which cell regions the drug affects, e.g., rounding vs. elongation, lost protrusions, altered curvature.
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+- New dataset, ATLAS-1: Drug-treated 3D melanoma cells in collagen (control, nocodazole, blebbistatin) to evaluate both performance and interpretability on real data. Available on Hugging Face 🤗 
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+- Higher accuracy than black-box models. 
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+- Plug-in head: Works with popular point-cloud backbones.
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+Try it for yourself:
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+🌐 Project page + live demo: https://sentinal4d.github.io/PointMIL/
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+💾 Code: https://github.com/Sentinal4D/PointMIL/
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+🧪 Dataset (ATLAS-1): https://sentinal4d.github.io/ATLAS-1/
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+If you work on cell morphology, drug discovery, or explainable 3D AI, we’d love your feedback and collaborations.
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+#ICCV2025 #Sentinal4D #PointMIL #3D #CellBiology #Morphology #ExplainableAI #XAI #ComputerVision #MIL #OpenScience