Year: 2024
Type: conference-paper
Abstract: Current deep learning methods in histopathology are limited by the small amount of available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor budding quantification performed using H&E-stained slides is crucial for cancer staging and prognosis but is subject to labor-intensive annotation and human bias. Thus, acquiring a large-scale, fully annotated dataset for training a tumor budding (TB) segmentation/detection system is difficult. Here, we present a DatasetGAN-based approach that can generate essentially an unlimited number of images with TB masks from a moderate number of unlabeled images and a few annotated images. The images generated by our model closely resemble the real colon tissue on H&E-stained slides. We test the performance of this model by training a downstream segmentation model, UNet++, on the generated images and masks. Our results show that the trained UNet++ model can achieve reasonable TB segmentation performance, especially at the instance level. This study demonstrates the potential of developing an annotation-efficient segmentation model for automatic TB detection and quantification.
DOI: 10.1117/12.3006418
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2698949731, Efficient and cost-effective breast cancer risk stratification using whole slide histopathology images, Development of quantitative tools to predict patients with difficult intubation to minimize treatment related complications, Computer-assisted diagnosis of ear pathologies by combining digital otoscopy with complementary data using machine learning, An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.
OpenAlex ID: W4393575612