improved U-Net for chest X-ray images segmentation
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Henan University of Technology,Nanyang Central Hospital
- Organisation type
- Academia,Government
- Country
- China
- Published
- 23 May 2022
- Authors
- Wufeng Liu, Jiaxin Luo, Yan Yang, Wenlian Wang, Junkui Deng & Liang Yu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Medicine, Vision
- Task
- Visual question answering, Medical diagnosis
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 182,517,760 tokens
- Epochs
- 70
"The batch size is set to 64. Max epochs are set to 70." "image size was adjusted to 256 * 256"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce RTX 3060,Intel Core i5-11600K
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Automatic lung segmentation in chest X-ray images using improved U-Net
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
improved U-Net for chest X-ray images segmentation was published by Henan University of Technology,Nanyang Central Hospital, in the country recorded as China, during May 2022. It comes out of an organisation categorised as academia,Government.
It works in the domain of Medicine, Vision, and is recorded as performing the task of visual question answering, Medical diagnosis.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training consumed a corpus of around 182,517,760 tokens of text.
Answers
improved U-Net for chest X-ray images segmentation — common questions
improved U-Net for chest X-ray images segmentation— what is it used for?
It works in the domain of Medicine, Vision, and is recorded as handling the task of visual question answering, Medical diagnosis. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
improved U-Net for chest X-ray images segmentation— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
improved U-Net for chest X-ray images segmentation— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
improved U-Net for chest X-ray images segmentation— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
improved U-Net for chest X-ray images segmentation— who created it?
It was published by Henan University of Technology,Nanyang Central Hospital, based in China, an organisation categorised as academia,Government.
improved U-Net for chest X-ray images segmentation— when was it released?
It was published in May 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.