MSc. Artificial Intelligence in Medicine, University of Bern.
Technologies used: Dinov2, Transformers, PyTorch, Wandb.
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Technologies used are PyTorch, Transformers,BitNet, BERT, HuggingFace, Wandb.
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Technologies used are Spacy, NLTK, BERT, GPT, Label-Studio, Wandb, HuggingFace.
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Given a clinical dataset with bladder cancer patients, we identify 7 subpopulations. We infer from proteomics and metadata, the most informative features.
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Pancreatic Cancer Risk Genes simulated dataset using R Studio and additional plots for GWAS study plots.
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We apply pivot calibration, Region-Growing segmentation with seed, Point Cloud Registration and U-NET segmentation deep learning for reference.
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Based on NERF paper, we reconstruct the point cloud from views of complex scenes using 5D coordinates.
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Reinforcement Learning algorithms for model-free control of cart pole in openai gym.
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