Image List Page
Quick Annotator currently does not support reading whole slides images (WSI) directly, but QA does provide a script that divides WSIs into smaller image tiles:
Open cli folder and use extract_tiles_from_wsi_openslide.py to divide WSI into smaller tiles
cd quick_annotator\cli
python extract_tiles_from_wsi_openslide.py
In Image List Page, the user could view the thumbnail images in the projects together with meta information. After dividing WSIs into smaller image tiles, the user uploads them into QA in 1. Drop files here or click to upload.
The red boxed rectangle is the Notification Window where QA provides notification logs for status updates. QA's other web pages employ this feature as well.asfd
The user follows instructions in order shown on the Image List Page
Images tiles are sub-divided into 256 x 256, amenable to deep learning. The user can decide whether they want to remove white backgrounds when dividing these image tiles. This function is useful when there are large areas of white backgrounds. ("E.g. TMA spot")
Model 0 is as know as an autoencoder. A u-net consisting of a block depth of 5 layers and 113,306 parameters was trained on these patches in an auto-encoding fashion. Model weights were optimized to reproduce the input as output with high fidelity. This helped create an initialized base model that was subsequently used in the downstream supervised training.
A UMAP embedding plot is generated by Embed Patches with the current latest model. The user can View Embedding plot in the embedding page, which helps to select patches for annotation which are dispersed in the model space. The embedding page is a 2D representation of all patches in the system, where patches perceived to be similar by the model are plotted closely together.