Deep emb e ddings and logistic regression for rapid active learning in histopathological images

Background and Objective: Recognizing different tissue components is one of the most fundamental and essential works in digital pathology. Current methods are often based on convolutional neural net-works (CNNs), which need numerous annotated samples for training. Creating large-scale histopathologi-cal datasets is labor-intensive, where interactive data annotation is a potential solution. Methods: We propose DELR (Deep Embedding-based Logistic Regression) to enable rapid model training and inference for histopathological image analysis. DELR utilizes a pretrained CNN to encode images as compact embeddings with low computational cost. The embeddings are then used to train a Logistic Regression model efficiently. We implemented DELR in an active learning framework, and validated it on three histopathological problems (binary, 4-category, and 8-category classification challenge for lung, breast, and colorectal cancer, respectively). We also investigated the influence of active learning strategy and type of the encoder. Results: On all the three datasets, DELR can achieve an area under curve (AUC) metric higher than 0.95 with only 100 image patches per class. Although its AUC is slightly lower than a fine-tuned CNN coun-terpart, DELR can be 536, 316, and 1481 times faster after pre-encoding. Moreover, DELR is proved to be compatible with a variety of active learning strategies and encoders. Conclusions: DELR can achieve comparable accuracy to CNN with rapid running speed. These advantages make it a potential solution for real-time interactive data annotation. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study introduces a new method, DELR, which utilizes deep embedding-based logistic regression for rapid model training and inference in histopathological image analysis. It achieves good validation results on three histopathological problems and demonstrates faster speed compared to CNN.