openmedical/registry
← registry

Unsuprevised_Seg_via_CNN

imported

software/unsuprevised-seg-via-cnn

An unsupervised (or self-supervised) loss function for binary image segmentation (TensorFlow)

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
image-segmentation-tensorflow · keras-tensorflow · medical-image-segmentation · unsupervised-learning
Regulatory
unknown
built by · 1

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • 🩺 Diabetes Prediction & Patient Stratification: A Machine Learning Approach.

  • Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

  • AI-powered chest X-ray pneumonia detection with 86% accuracy and 96.4% sensitivity, validated on an independent (cross-operator) cohort of 485 pediatric samples. Built with TensorFlow & FastAPI.

  • DeepEEGkeras-tensorflow

    Deep Learning with Tensor Flow for EEG MNE Epoch Objects

  • Glands-detectionkeras-tensorflow

    🧠 A deep learning algorithm based on convolutional neural networks to detect glandular cells in digitalized biopsies of the prostate.

  • RANZCR-CLiPkeras-tensorflow

    Multi-label CV classification with 87% accuracy in detecting correct catheter placement in COVID-19 patient chest x-rays under the Royal Australian and New Zealand College of Radiologists Catheter…

sources
  1. api.github.com/repos/junyuchen245/Unsuprevised_Seg_via_CNN
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2021-12-03, 71 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/31.json→ .entries["unsuprevised-seg-via-cnn"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.