Update for repo name change

Author: manthey

paper/paper.md

@@ -1,5 +1,5 @@
 ---
-title: 'WSI Superpixel Guided Labeling'
+title: 'Histomics Label'
 tags:
   - Python
   - histology
@@ -50,15 +50,15 @@ bibliography: paper.bib
 
 # Summary
 
-`WSI Superpixel Guided Labeling` facilitates active learning on whole slide images.  It has a user interface built on top of the HistomicsUI [@histomicsui] base and deployed as part of the Digital Slide Archive [@Gutman2017, @digitalslidearchive], and uses the HistomicsTK [@histomicstk] tool kit as part of the process.
+`Histomics Label` facilitates active learning on whole slide images.  It has a user interface built on top of the HistomicsUI [@histomicsui] base and deployed as part of the Digital Slide Archive [@Gutman2017, @digitalslidearchive], and uses the HistomicsTK [@histomicstk] tool kit as part of the process.
 
 Users label superpixel regions or other segmented areas of whole slide images to be used as classification input for machine learning algorithms.  An example algorithm is included which generates superpixels, features, and machine learning models for active learning on a directory of images.  The interface allows bulk labeling, labeling the most impactful superpixels to improve the model, and reviewing labeled and predicted categories.
 
 # Statement of need
 
 One of the limitations in generating accurate models is the need for labeled data.  Given a model and a few labeled samples, there are a variety of algorithms that can be used to determine what samples should be additionally labeled to most efficiently improve the model.  To actually get labeled data, this prediction of which samples to label needs to be combined with an efficient workflow so that the domain expert can use their labeling time in the most effective manner possible.
 
-`WSI Superpixel Guided Labeling` provides a user interface and workflow for this guided labeling process.  Given a set of whole slide images, the images are segmented based on a some user choices.  This segmentation is the basis for labeling.  The user can specify any number of label categories, including labels that will be excluded from training (for instance, for segmented regions whose categories cannot be accurately determined).  After labeling a few initial segments, a model is generated and used to both predict the category of all segments and the segments that would result in the best improvement in the model if they were also labeled.  The user can retrain the model at any time and review the results of both the predictions and other users.
+`Histomics Label` provides a user interface and workflow for this guided labeling process.  Given a set of whole slide images, the images are segmented based on a some user choices.  This segmentation is the basis for labeling.  The user can specify any number of label categories, including labels that will be excluded from training (for instance, for segmented regions whose categories cannot be accurately determined).  After labeling a few initial segments, a model is generated and used to both predict the category of all segments and the segments that would result in the best improvement in the model if they were also labeled.  The user can retrain the model at any time and review the results of both the predictions and other users.
 
 For development, the initial segmentation uses superpixels generated with the SLIC [@SLIC2012] algorithm.  These are computed on whole slide images in a tiled manner so that they can work on arbitrarily large images, and the tile boundaries are properly handled to avoid visible artifacts.  Once generated, segments are represented in one of two ways, either as two-dimensional patches, each centered in a fixed-sized square of pixels with non-segment pixels set to black, or as one-dimensional vectors, such as those generated from the huggingface UNI [@huggingface2024uni] foundation model.  One of two basic models is trained based upon the segment representation.  For two-dimensional patches, the model to be trained is a small-scale CNN implemented in tensorflow/keras or torch.  For one-dimensional vectors, the model to be trained is a single-layer linear classifier.  The certainty criteria for which segments should be labeled next can also be selected, and includes confidence, margin, negative entropy, and the BatchBALD [@batchbald2019] algorithm.