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LWU-Net approach for Efficient Gastro-Intestinal Tract Image Segmentation in Resource-Constrained Environments

Sai, M. J.; Punn, N. S.

2023-12-05 gastroenterology
10.1101/2023.12.05.23299425 medRxiv
Show abstract

This paper introduces a Lightweight U-Net (LWU-Net) method for efficient gastro-intestinal tract segmentation in resource-constrained environments. The proposed model seeks to strike a balance between computational efficiency, memory efficiency, and segmentation accuracy. The model achieves competitive performance while reducing computational power needed with improvements including depth-wise separable convolutions and optimised network depth. The evaluation is conducted using data from a Kaggle competition-UW Madison gastrointestinal tract image segmentation, demonstrating the models effectiveness and generalizability. The findings demonstrate that the LWU-Net model has encouraging promise for precise medical diagnoses in resource-constrained settings, enabling effective image segmentation with slightly less than a fifth of as many trainable parameters as the U-Net model.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.