This project implements and bechnmarks two state-of-the-art models for image segmentation. Namely U-Net and a 2D adaption of V-Net

Detailed results and instructions to replicate our results can be found in our demonstration notebook.

To run the notebook install ✨ pixi ✨ package via conda. Next run pixi shell.

Our UNet implementation is located here and our V-Net adaption can be found here. The final report can be found here

Dataset ISIC 2018 Challenge Task 1: Lesion segmentation: Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, Allan Halpern: "Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)", 2018; https://arxiv.org/abs/1902.03368

U-Net Architecture O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation.” [Online]. Available: https://arxiv.org/abs/1505.04597

V-Net Architecture F. Milletari, N. Navab, and S.-A. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation.” [Online]. Available: https://arxiv.org/abs/1606.047979