Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds
Authors: Jadie Adams, Shireen Elhabian
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a benchmark of state-of-the-art point cloud deep networks on the SSM task, revealing their limited robustness to clinical challenges such as noisy, sparse, or incomplete input and limited training data. |
| Researcher Affiliation | Academia | Jadie Adams & Shireen Y. Elhabian Scientific Computing and Imaging Institute Kalhert School of Computing University of Utah, USA {jadie,shireen}@sci.utah.edu |
| Pseudocode | No | The paper describes the architecture and loss functions but does not include explicit pseudocode or an algorithm block. |
| Open Source Code | Yes | The source code is provided at https://github.com/jadie1/Point2SSM. |
| Open Datasets | Yes | We utilize three challenging organ mesh datasets of various sample sizes to benchmark the performance of Point2SSM and the comparison methods: spleen (Simpson et al., 2019) (40 shapes), pancreas (Simpson et al., 2019) (272 shapes), and left atrium of the heart (1096 shapes). |
| Dataset Splits | Yes | The datasets are randomly split into a training, validation, and test set using an 80%, 10%, 10% split. |
| Hardware Specification | Yes | A 4x TITAN V GPU was used to train all models. |
| Software Dependencies | No | The paper mentions 'Adam optimization' but does not specify version numbers for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | In all experiments, we set N = 1024, L = 128, M = 1024, and batch size B = 8, unless otherwise specified. Adam optimization with a constant learning rate of 0.0001 is used, and model training is run until convergence via validation assessment. Specifically, a model is considered converged if the validation CD has not improved in 100 epochs. |