TC-CoNet

Research applicability

Supports researchers studying medical-image segmentation; no clinical deployment or comparative accuracy endorsement is made.

Getting started

Editorial, untested starting point: inspect the README environment and data-preparation sections; use a permitted dataset, adapt the example local paths, then review preprocessing/training/inference scripts before execution. Have domain experts assess outputs.

Maintenance

Documentation read on 2026-10-17; maintenance and runtime compatibility not tested.

License

Licensing and third-party data rights were not independently audited; check before reuse.

Workflow & human oversight

Workflow level intentionally unspecified for this reusable resource. Researchers select inputs, validate outputs and retain responsibility for interpretation.

Workflow level is not an evidence score; higher is not better evidence.

Approach

README organizes architecture and trainer scripts for several medical-image tasks and links ACDC, Synapse multi-organ CT and brain-tumor datasets; it acknowledges borrowed nn-UNet code.

Limitations

Primary README only; associated journal paper, training data and code were not evaluated. No segmentation accuracy or clinical utility is verified. Documented environment is old (Python 3.7, PyTorch 1.7.1, Windows 10); present compatibility is unknown. Dataset permissions and sensitive medical-data governance require separate review.

Reproducibility

Primary repository documentation inspected; no installation, code execution, dataset inspection or independent outcome reproduction.

Adoption requirements

README specifies Python 3.7, PyTorch 1.7.1 and Windows 10 and refers to nn-UNet for dataset configuration. Dataset access, appropriate medical-data permissions and a compatible environment are required.