Frequently Asked Questions
We collect feedbacks and sorted them into a FAQ section here:
The general guides for installing Pytorch can be summarized as follows:
- Check your NVIDIA GPU Compute Capability @ https://developer.nvidia.com/cuda-gpus
- Download CUDA Toolkit @ https://developer.nvidia.com/cuda-downloads
- Install PyTorch command can be found @ https://pytorch.org/get-started/locally/
- For a given project, the database contains the latest model id under the "iteration" column.
- A model_id of -1 means no AI model has been trained at all.
- A model_id of 0 represents the initial autoencoder.
- A model_id of >=1 represents the deep learning iteration.
- The get_latest_model_id function will pull this information from the database.
Since Quick Annotator currently does not support WSIs, the users need to divide WSI into smaller image tiles. QA provides a script cli\extract_tiles_from_wsi_openslide.py, which imports OpenSlide.
However, we received feedbacks that many Windows users had difficulty in OpenSlide. Therefore, we provide a detailed tutorial for installing and importing OpenSlide in Windows.
- Find and install OpenSlide Python with proper python version @ https://pypi.org/project/openslide-python/
- Find and install OpenSlide Window Binaries @ https://openslide.org/download/
- Add openslide\bin to system environment path. (Many of our testers forget to add OpenSlide to path, so they could not import OpenSlide properly.)
Control Panel -> System Properties -> Advanced -> Environment Variables
-> System variables -> *Path*
The efficiency improvements of QA differs from the histologic structures that use employs. In a recent paper, we presented an efficiency plot for pancreatic cell nuclei, colon tubules, breast cancer epithelium, which corresponds to small, medium, and large scales tissues.
Efficiency metric over time demonstrating the improvement in speed afforded by QA in annotating (A) nuclei, (B) tubules, and (C) epithelium. The x-axis is the human annotation time in minutes, and the y-axis is the annotation speed in terms of annotated histologic structures per minute. The trend of performance improvements varies per use case, with (a) the nuclei showing a consistent improvement in time, (b) the tubule performance plateauing after annotating a few structures, and (c) the epithelium requiring several additional iterations before reaching its plateau. These plateaus indicate the DL model is sufficiently trained to produce suggestions agreeable to the user.
Quick Annotator is not designed to train a perfect histologic deep learning model. However, QA's essential purposes are to help pathologists and doctors rapidly bootstrap annotations so that they could start post annotation studies (e.g., biomarker analysis). Simultaneously, these annotations could be provided as training data for future studies in the community. It is mostly impossible to train a perfect model with a minimal amount of data in a short period of time.
It is important to note that the user is the final arbiter of an acceptable annotation and always has the ability to manually adjust any pixel they are in disagreement with. We provide a demo of a QA's classifier that gives a near perfect suggestion, and the user only needs to make the minimal manual correction before accepting.
A user is annotating colon tubules image slides.