MechDataExtractor
importedtherapeutics/mechdataextractor
Image pretreatment for OSCR tasks especially for task related to molecular identity recognition from chemical reaction mechanisms.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Therapeutics
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- maintained
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/ting2025/MechDataExtractor
- Documentation
- unknown
- Tags
- cheminformatics · image-segmentation · machine-learning · optical-chemical-structure-recognition
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- ACC-UNetimage-segmentation
ACC-UNet is A Completely Convolutional UNet model inspired from transformer-based UNets
- AgileFormerimage-segmentation
This the repo for the paper tiltled "AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation"
- AI-for-healthcareimage-segmentation
The impact of Artificial Intelligence in improving healthcare facilities is increasing significantly. This repository provides implementation of different Deep Learning and Machine Learning…
- ai4elifeimage-segmentation
This data-centric AI repository implements a robust deep learning method (LFBNet) for fully automated tumor segmentation in whole-body [18]F-FDG PET/CT images.
- aladdin_cmr_laimage-segmentation
Source code for Aladdin, a complete workflow for 3D MRI left atrium motion analysis
- angiodysplasia-segmentationimage-segmentation
Wining solution and its further development for MICCAI 2017 Endoscopic Vision Challenge Angiodysplasia Detection and Localization
- api.github.com/repos/ting2025/MechDataExtractorretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2025-06-16, 7 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 →
/v1/entries/20.json→ .entries["mechdataextractor"]
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