Zhi-Qi Cheng, Jyoti Kini, Yujia Hu, Rolf Krause, Diego Pizzagalli, Raffaella Fiamma Cabini, Deborah Barkauskas, Guangyu Chen, David Cicchetti, Judith Drazba, Rodrigo Fernandez-Gonzalez, Raymond Hawkins, and 11 more
We lifted 9 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| rcabini/CBVCC | canonical | 0 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| clean_model_name | Not yet run | rcabini/CBVCC/metrics/roc_curves.py code served (permissive licence) · get_code("a84537303b1cb3ce") |
| compute_score_by_snr | Not yet run | rcabini/CBVCC/metrics/snr_curves.py code served (permissive licence) · get_code("0f0b42b010a85e6c") |
| compute_score_per_cell_count | Not yet run | rcabini/CBVCC/metrics/ncell_curves.py code served (permissive licence) · get_code("1c5a3555364fe2f2") |
| evaluate_models | Not yet run | rcabini/CBVCC/metrics/overall_metrics.py code served (permissive licence) · get_code("518fd11990fae12d") |
| load_gt | Not yet run | rcabini/CBVCC/metrics/upload_files.py code served (permissive licence) · get_code("d377a59a3235b985") |
| load_paths | Not yet run | rcabini/CBVCC/metrics/compute_quality_metrics.py code served (permissive licence) · get_code("bfc3eb5075c265eb") |
| load_track_counts | Not yet run | rcabini/CBVCC/metrics/upload_files.py code served (permissive licence) · get_code("9bc7b3e836770ca1") |
| plot_roc_curves | Not yet run | rcabini/CBVCC/metrics/roc_curves.py code served (permissive licence) · get_code("4f07a51467646c98") |
| preprocess_submission | Not yet run | rcabini/CBVCC/metrics/upload_files.py code served (permissive licence) · get_code("f86acc357e1aa5bf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The classification of microscopy videos capturing complex cellular behaviors is crucial for understanding and quantifying the dynamics of biological processes over time. However, it remains a frontier in computer vision, requiring approaches that effectively model the shape and motion of objects without rigid boundaries, extract hierarchical spatiotemporal features from entire image sequences rather than static frames, and account for multiple objects within the field of view. To this end, we organized the Cell Behavior Video Classification Challenge (CBVCC), benchmarking 35 methods based on three approaches: classification of tracking-derived features, end-to-end deep learning architectures to directly learn spatiotemporal features from the entire video sequence without explicit cell tracking, or ensembling tracking-derived with image-derived features. We discuss the results achieved by the participants and compare the potential and limitations of each approach, serving as a basis to foster the development of computer vision methods for studying cellular dynamics.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2601.10250")
get_code_for_paper("2601.10250")
have("2601.10250")
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