TreeMoCo: Contrastive Neuron Morphology Representation Learning
Authors: Hanbo Chen, Jiawei Yang, Daniel Iascone, Lijuan Liu, Lei He, Hanchuan Peng, Jianhua Yao
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test Tree Mo Co on 2403 high-quality 3D neuron reconstructions of mouse brains from three different public resources. Our results show that Tree Mo Co is effective in both classifying major brain cell-types and identifying sub-types. |
| Researcher Affiliation | Collaboration | Hanbo Chen , Tencent AI Lab hanbochen@tencent.com Jiawei Yang , University of California, Los Angeles jiawei118@ucla.edu Daniel Maxim Iascone University of Pennsylvania, Philadelphia daniel.iascone@pennmedicine.upenn.edu Lijuan Liu Southeast University, Nanjing juan-liu@seu.edu.cn Lei He University of California, Los Angeles lhe@ee.ucla.edu Hanchuan Peng Southeast University, Nanjing h@braintell.org Jianhua Yao Tencent AI Lab jianhuayao@tencent.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https: //github.com/Tencent AILab Healthcare/Neuron Representation. |
| Open Datasets | Yes | We download data from 3 public resources: (1) BICCN f MOST data from Brain Image Library (BIL, https://download.brainimagelibrary.org/biccn/zeng/luo/f MOST/) [28], (2) Janelia Mouse Light (JML, http://mouselight.janelia.org/) [44], and (3) Allen Cell Types (ACT, https://celltypes.brain-map.org/) [1]. |
| Dataset Splits | No | The paper specifies a train/test split (80% training, 20% testing) but does not explicitly mention a separate validation set or split percentage for validation data. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run the experiments within the provided text. |
| Software Dependencies | No | The paper refers to implementation details and settings in Appendix B.1 and B.2, but the provided text does not explicitly list specific software dependencies with version numbers. |
| Experiment Setup | Yes | All models share the same 29-d input features and are trained for 100 epochs. We set the number of neighbors to 20 for BIL-6 and ACT datasets and 5 for the JML-4 dataset since a minority class might only have 12 samples in total in JML-4. |