ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading

• URL: http://arxiv.org/abs/2606.21116v1
• Date: Fri, 19 Jun 2026 05:37:42 GMT
• Title: ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading
• Abstract summary: We release ConnectomeBench2, a unified multi-species dataset of over 716,485 expert-labeled proofreading decisions.<n>A single Vision Transformer with shared encoders for mesh geometry and electron microscopy reaches human-level accuracy across species for split error correction and merge error identification.<n>The benchmark provides the infrastructure to train and evaluate increasingly capable vision models for connectomic proofreading.
• Score: 0.1527216171684335
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Proofreading--correcting segmentation errors in 3D brain reconstructions--is the rate-limiting step in synapse-resolution connectomics. We release ConnectomeBench2, a unified multi-species dataset of over 716,485 expert-labeled proofreading decisions with >4,500,000 associated images spanning four major open connectomes (mouse, human, zebrafish, fly), spanning both split and merge error correction. Trained on this dataset, a single Vision Transformer with shared encoders for mesh geometry and electron microscopy reaches human-level accuracy across species for split error correction and merge error identification, with performance scaling with data size and modality. Beyond accuracy, we show that the model is well-calibrated within distribution, that measures of distribution distance predict where calibration and accuracy will degrade on unseen data, and that connectomics-specific pretraining and active learning-based sample selection show potential to substantially reduce the labeling effort needed to extend to new species and brain regions. The benchmark provides the infrastructure to train and evaluate increasingly capable vision models for connectomic proofreading. Data and code availability. The ConnectomeBench2 dataset is released on Hugging Face at https://huggingface.co/datasets/jeffbbrown2/ConnectomeBench2. The accompanying codebase is available on GitHub at https://github.com/timfarkas/ConnectomeBench2.

Related papers

• Statistical Symmetry Release for Equivariant Quantum Learning [12.042012916861829]
  Hard symmetry constraints reduce model complexity, but can also erase label information.<n> Statistical symmetry release determines when finite data and quantum measurements justify relaxing such a constraint.
  arXiv  Detail & Related papers  (2026-09-10T12:38:27Z)
• SurgFormer: Scalable Learning of Organ Deformation with Resection Support and Real-Time Inference [14.49835131603816]
  We introduce SurgFormer, a multiresolution gated transformer for data driven soft tissue simulation on volumetric meshes.<n>High fidelity biomechanical solvers are often too costly for interactive use, so we train SurgFormer on solver generated data to predict nodewise displacement fields.<n>SurgFormer achieves strong accuracy with favorable efficiency, making it a practical backbone for both tasks.
  arXiv  Detail & Related papers  (2026-03-06T18:29:47Z)
• The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging [3.597779662054083]
  Model merging under unseen test-time distribution shifts often renders naive strategies, such as mean averaging unreliable.<n>We introduce an entropy-adaptive, fully online model-merging method that yields a batch-specific merged model via only forward passes.<n>We extensively evaluate our method with state-of-the-art baselines using two backbones across nine medical and natural-domain generalization image classification datasets.
  arXiv  Detail & Related papers  (2026-02-24T21:06:19Z)
• ConnectomeBench: Can LLMs Proofread the Connectome? [0.4999814847776097]
  We introduce ConnectomeBench, a benchmark evaluating large language model (LLM) capabilities in three critical proofreading tasks.<n>We evaluate proprietary multimodal LLMs including Claude 3.7/4 Sonnet, o4-mini, GPT-4.1, GPT-4o, as well as open source models like InternVL-3 and NVLM.<n>Our results demonstrate that current models achieve surprisingly high performance in segment identification.<n>While the best models still lag behind expert performance, they demonstrate promising capabilities that could eventually enable them to augment and potentially replace human proofreading in connectomics.
  arXiv  Detail & Related papers  (2025-10-31T02:20:38Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Hyperbolic Vision Transformers: Combining Improvements in Metric Learning [116.13290702262248]
  We propose a new hyperbolic-based model for metric learning. At the core of our method is a vision transformer with output embeddings mapped to hyperbolic space. We evaluate the proposed model with six different formulations on four datasets.
  arXiv  Detail & Related papers  (2022-03-21T09:48:23Z)
• Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing [97.44152794234405]
  Unsupervised cross-modal hashing (UCMH) has become a hot topic recently. In this paper, we devise a deep graph-neighbor coherence preserving network (DGCPN) DGCPN regulates comprehensive similarity preserving losses by exploiting three types of data similarities.
  arXiv  Detail & Related papers  (2020-12-25T07:40:59Z)
• Online Missing Value Imputation and Change Point Detection with the Gaussian Copula [21.26330349034669]
  Missing value imputation is crucial for real-world data science. We develop an online imputation algorithm for mixed data using the Gaussian copula.
  arXiv  Detail & Related papers  (2020-09-25T16:27:47Z)
• Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction [65.44752447868626]
  We propose collaborative boundaryaware context encoding networks called AEP-Net for error prediction task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. The AEP-Net achieves an average DSC of 0.8358, 0.8164 for error prediction task, and shows a high Pearson correlation coefficient of 0.9873.
  arXiv  Detail & Related papers  (2020-06-25T12:42:01Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.