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patients_to_slices

Syntologyentry name in harvested coderead from the graph 2026-09-24

patients_to_slices appears in the code Syntology harvested for 10 papers, as 8 distinct code bodies found in 16 places (a place is one code body under one paper). At least one of them ran in 9 of the papers; 3 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named patients_to_slices do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 7 of the 8 distinct code bodies named patients_to_slices; 1 is unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

7ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
0ran
1unverified
3fingerprinted

Licence is a property of each copy, so it is counted per place: 11 of the 16 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

10 papers shown of 10, newest first; 16 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation 16 Feb 2024 identical code first harvested elsewhere cab70404b0657b24 ran · honoured contract licence of this copy not recorded
Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation 16 Feb 2024 identical code first harvested elsewhere f53c4c3922632c64 ran · honoured contract licence of this copy not recorded
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation 11 Feb 2024 identical code first harvested elsewhere f53c4c3922632c64 ran · honoured contract licence of this copy not recorded
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation 7 Feb 2024 ziyangwang007/mamba-unet/code/train_fully_supervised_2D_VIM.py cab70404b0657b24 ran · honoured contract Apache-2.0 (permissive)
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation 7 Feb 2024 ziyangwang007/mamba-unet/code/train_Semi_Mamba_UNet.py f53c4c3922632c64 ran · honoured contract Apache-2.0 (permissive)
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation 7 Feb 2024 ziyangwang007/mamba-unet/code/train_uncertainty_aware_mean_teacher_2D.py 333ad2f6c5aa26e7 ran · honoured contract fingerprinted Apache-2.0 (permissive)
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation 1 May 2023 deepmed-lab-ecnu/bcp/code/KDE_demo.py b4b3d82f0caa5a95 unverified MIT (permissive)
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast 5 Apr 2023 identical code first harvested elsewhere ec1da6066ce5a8be ran · honoured contract licence of this copy not recorded
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast 5 Apr 2023 identical code first harvested elsewhere 9d53ee5a03e70898 ran · honoured contract fingerprinted licence of this copy not recorded
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective 3 Feb 2023 charlesyou999648/arco/code/train_arco_2d.py a574010165a9226d ran · honoured contract no licence file found · pointer only
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective 3 Feb 2023 charlesyou999648/arco/code/train_arco_3d.py ec1da6066ce5a8be ran · honoured contract no licence file found · pointer only
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels 27 Sep 2022 identical code first harvested elsewhere a574010165a9226d ran · honoured contract licence of this copy not recorded
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation 6 Jun 2022 identical code first harvested elsewhere ec1da6066ce5a8be ran · honoured contract licence of this copy not recorded
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation 6 Jun 2022 charlesyou999648/action/code/train_3D_action++.py 9d53ee5a03e70898 ran · honoured contract fingerprinted no licence file found · pointer only
Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer 9 Dec 2021 identical code first harvested elsewhere 89e1d9b882bf8bbe ran · honoured contract fingerprinted licence of this copy not recorded
Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency 13 Dec 2020 HiLab-git/SSL4MIS/code/train_uncertainty_rectified_pyramid_consistency_2D.py 89e1d9b882bf8bbe ran · honoured contract fingerprinted MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections