SCCS: Deep Neural Spectral Clustering for Self-Supervised Subcellular Structure Segmentation
Authors: Jimao Jiang, Diya Sun, Tianbing Wang, Yuru Pei
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is evaluated on a publicly available volumetric electron microscopy dataset. Experiments demonstrate the effectiveness and performance gains of the proposed SCCS over the state-of-the-art in discovering a variety of subcellular structures. |
| Researcher Affiliation | Academia | 1 School of Intelligence Science and Technology, Key Laboratory of Machine Perception (MOE), State Key Laboratory of General Artificial Intelligence, Peking University, Beijing 100871, China 2 Institute of Artificial Intelligence, Peking University People s Hospital, Peking University, Beijing 100871, China EMAIL |
| Pseudocode | No | The paper describes its methodology through detailed textual explanations and diagrams, such as Figure 1: "Pipeline overview of our SCCS.", but it does not contain a specific block or section explicitly labeled as 'Pseudocode' or 'Algorithm' with structured steps. |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology described in this paper is released or provide a link to a code repository. The text mentions using a dataset from Open Organelle but not releasing their own code. |
| Open Datasets | Yes | We have evaluated the proposed SCCS on dominant subcellular structure segmentation from primary mouse pancreatic islets β cells of the Beta Seg dataset (Heinrich et al. 2021; M uller et al. 2021). |
| Dataset Splits | Yes | We use the first three cell volumes for training and the remaining volume for testing. |
| Hardware Specification | Yes | The training is performed on a PC with an NVIDIA RTX 2080Ti GPU, consuming 6 hours with 6,000 iterations. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and MAE-based feature extractor but does not specify version numbers for any programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | We use the Adam optimizer with a momentum of 0.9 and 0.999. For training the MAE-based feature extractor, we use a learning rate of 1e-5 and a batch size of 32. We set the learning rate to 0.01 with a batch size of 1 for training the neural spectral clustering model. The MAE-based features channel number q is set to 192. κ in the RBF kernelbased affinity computation is set to 2. We retain u = 12 approximated spectral bases. We set the cluster number k to 8. Hyperparameter α in affinity matrix computation is set to 4. The hyperparameter µ in the spectral embedding loss Lspe is set to 1. The scalar threshold η and coefficient ν in Lsmo are both set to 0.1. The hyperparameters γ1 and γ2 in the loss function L are set to 1 and 0.1 to balance the criteria of spectral embedding and the regularized clustering. The LGC-based spectral embedding module consists of three linear graph convolutional layers with 384 96, 96 48, and 48 12 weight matrices. The MLP-based clustering modules consist of two fully connected layers with 12 12 and 12 8 weight matrices. |