Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Topology-Preserving Deep Image Segmentation
Authors: Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is empirically validated by comparing with state-of-the-arts on natural and biomedical datasets with fine-scale structures. It achieves superior performance on metrics that encourage structural accuracy. In particular, our method significantly outperforms others on the Betti number error which exactly measures the topological accuracy. |
| Researcher Affiliation | Academia | Xiaoling Hu1, Li Fuxin2, Dimitris Samaras1 and Chao Chen1 1Stony Brook University 2Oregon State University |
| Pseudocode | No | The paper does not include structured pseudocode or algorithm blocks. It describes the computational steps and gradients in paragraph text. |
| Open Source Code | No | The paper does not contain any explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We evaluate our method on six natural and biomedical datasets: CREMI7, ISBI12 [4], ISBI13 [3], Crack Tree [48], Road [28] and DRIVE [39]. The citations for these datasets imply their public availability. |
| Dataset Splits | Yes | For all datasets, we use a three-fold cross-validation and report the mean performance over the validation set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. It only details the network architecture and training patch sizes. |
| Software Dependencies | No | The paper mentions using a 'deep neural network' and 'cross-entropy loss' but does not specify any software libraries or versions (e.g., PyTorch 1.9, TensorFlow 2.x) with version numbers. |
| Experiment Setup | Yes | Our network contains six trainable weight layers, four convolutional layers and two fully connected layers. The first, second and fourth convolutional layers are followed by a single max pooling layer of size 2 × 2 and stride 2 by the end of the layer. Particularly, because of the computational complexity, we use a patch size of 65 × 65 during all the training process. ... For convenience, we drop the weight of cross entropy loss and weight the topological loss with λ. ... In general, λ is at the magnitude of 1/10000. |