PathAL: An Active Learning Framework for Histopathology Image Analysis
| العنوان: | PathAL: An Active Learning Framework for Histopathology Image Analysis |
|---|---|
| المؤلفون: | Wenyuan Li, Jiayun Li, Zichen Wang, Jennifer Polson, Anthony E. Sisk, Dipti P. Sajed, William Speier, Corey W. Arnold |
| المصدر: | IEEE transactions on medical imaging, vol 41, iss 5 IEEE Trans Med Imaging |
| بيانات النشر: | eScholarship, University of California, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Male, Neural Networks, Noise measurement, Image Processing, Bioengineering, Article, Histopathology image analysis, Computer, Computer-Assisted, Engineering, Biomedical imaging, Clinical Research, active learning, Information and Computing Sciences, Annotations, Image Processing, Computer-Assisted, Humans, Training, Electrical and Electronic Engineering, noisy label detection, Cancer, Image segmentation, Radiological and Ultrasound Technology, Uncertainty, Prostatic Neoplasms, Computer Science Applications, Nuclear Medicine & Medical Imaging, curriculum learning, Task analysis, Neural Networks, Computer, Neoplasm Grading, Software |
| الوصف: | Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for analyzing histopathology images. However, training these models relies heavily on a large number of samples manually annotated by experts, which is cumbersome and expensive. In addition, it is difficult to obtain a perfect set of labels due to the variability between expert annotations. This paper presents a novel active learning (AL) framework for histopathology image analysis, named PathAL. To reduce the required number of expert annotations, PathAL selects two groups of unlabeled data in each training iteration: one "informative" sample that requires additional expert annotation, and one "confident predictive" sample that is automatically added to the training set using the model's pseudo-labels. To reduce the impact of the noisy-labeled samples in the training set, PathAL systematically identifies noisy samples and excludes them to improve the generalization of the model. Our model advances the existing AL method for medical image analysis in two ways. First, we present a selection strategy to improve classification performance with fewer manual annotations. Unlike traditional methods focusing only on finding the most uncertain samples with low prediction confidence, we discover a large number of high confidence samples from the unlabeled set and automatically add them for training with assigned pseudo-labels. Second, we design a method to distinguish between noisy samples and hard samples using a heuristic approach. We exclude the noisy samples while preserving the hard samples to improve model performance. Extensive experiments demonstrate that our proposed PathAL framework achieves promising results on a prostate cancer Gleason grading task, obtaining similar performance with 40% fewer annotations compared to the fully supervised learning scenario. An ablation study is provided to analyze the effectiveness of each component in PathAL, and a pathologist reader study is conducted to validate our proposed algorithm. |
| وصف الملف: | application/pdf |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c8fcd21a6d0c4389013696643c48029 https://escholarship.org/uc/item/5439t7wx |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....9c8fcd21a6d0c4389013696643c48029 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |