Frequently Asked Questions
We collect questions and feedbacks from Forum, 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 many 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 environemnt path. (Many of our testers forget to add OpenSlide to path, so they could not import OpenSlide properly.)
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 a number of additional iterations before reaching its plateau. These plateaus indicate the DL model is sufficiently trained to produce suggestions agreeable to the user.