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content/posts/KBhdiffusion_models_for_laproscopic_surgeries.md
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title = "Diffusion Models for Laproscopic Surgeries" | ||
author = ["Houjun Liu"] | ||
draft = false | ||
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What if we can use diffusion models to generate Laproscopic surgeries to train surgeons? | ||
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## Problem {#problem} | ||
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Asking dalle to just "generate a Laproscopic surgery" is not going to work. It will give you cartoons. | ||
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## Approach {#approach} | ||
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1. **text problem formulation**: "grasper grasp gallbladder" | ||
2. encode text into latents | ||
3. do diffusion with late fusion of latents | ||
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Data: Cholec T-45 | ||
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### Weighting {#weighting} | ||
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Scoring: Perception Prioritized Weighting + Prioritization for Signal-to-Noise | ||
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(Ho et al, 2020) | ||
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### Text {#text} | ||
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"[subject] [verb] [object] [surgical phase]" | ||
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"grasper grasp gallbladder in preparation" | ||
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### Model {#model} | ||
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Elucidated Imagen. Dall-E is very bad; Imagen-class models works better because (why?). | ||
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## Added Value to Physicians using Generated Images {#added-value-to-physicians-using-generated-images} | ||
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### Train a Classifier {#train-a-classifier} | ||
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Rendevouz Network: train a discriminator for procedure based on data augmented with generated images; 5% improvement. | ||
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### Medical Expert Survey {#medical-expert-survey} | ||
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"yo mr doctor man can you spot which one of these are generated?" | ||
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45% success rate. |