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Epistemic uncertainty, sometimes referred to as model uncertainty, describes what the model does not know because training data was not appropriate. Modelling epistemic uncertainty is crucial to prevent ill advised discussion making due to over confident models.
This research will show an innovative method useful in the segmentation of polyps during the screening phases of colonoscopies. To do this we have adopted a new approach which consists in merging the hybrid semantic network (HSNet) architecture model with the Reagion-wise(RW) as a loss function for the backpropagation process.
This projects uses video feeds from endoscopic procedures to identify polyps in the gastrointestinal tract and draw masks around them to aid doctors in identifying precursors of colorectal cancer.
Baseline model for BKAI-IGH_Neopolyp. Currently supports Unet and Attention Unet with VGG-16, MobilenetV2 and Efficientnet-B0 backbone. This repository is private therefore not a official implementation from BKAI.
Polyp segmentation tool utilizing U-Net for accurate medical image analysis, designed to enhance early detection and diagnosis of colorectal cancer. Features a user-friendly Streamlit web app for easy image processing and analysis, leveraging the Kvasir-SEG dataset for improved healthcare outcomes.