English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The use of Deep Learning (DL) based methods in medical histopathology imageshave been one of the most sought after solutions to classify, segment, anddetect diseased biopsy samples. However, given the complex nature of medicaldatasets due to the presence of intra-class variability and heterogeneity, theuse of complex DL models might not give the optimal performance up to the levelwhich is suitable for assisting pathologists. Therefore, ensemble DL methodswith the scope of including domain agnostic handcrafted Features (HC-F)inspired this work. We have, through experiments, tried to highlight that asingle DL network (domain-specific or state of the art pre-trained models)cannot be directly used as the base model without proper analysis with therelevant dataset. We have used F1-measure, Precision, Recall, AUC, andCross-Entropy Loss to analyse the performance of our approaches. We observedfrom the results that the DL features ensemble bring a marked improvement inthe overall performance of the model, whereas, domain agnostic HC-F remainsdormant on the performance of the DL models.

Ensembling Handcrafted Features with Deep Features: An Analytical Study for Classification of Routine Colon Cancer Histopathological Nuclei Images