Published January 17, 2023
| Version v1
Dataset
Open
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
Authors/Creators
- 1. Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA
- 2. Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- 3. Department of Immunology, Genetics and Pathology, Division of Cancer Precision Medicine, Uppsala University, Uppsala, Sweden
- 4. Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94305, USA
Description
This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".
The directory contains:
trained_model_1_weights.zip: Trained model weights for first place solution (Team 1).
trained_model_2_weights.zip: Trained model weights for second place solution (Team 2).
trained_model_3_weights.zip: Trained model weights for third place solution (Team 3).
trained_model_weights_baseline.zip: Trained model weights for the baseline model.
Files
trained_model_1_weights.zip
Additional details
Related works
- Is supplement to
- Preprint: 10.1101/2023.01.05.522764 (DOI)