.png){kind=icon}
返回
Focused active learning for histopathological image classification *
.png){kind=icon}
OA
AI
DOI：10.1016/j.media.2024.103162.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambiguities, and class imbalances, as commonly seen in the medical field. The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value. To address these challenges, we propose Focused Active Learning (FocAL), which combines a Bayesian Neural Network with Out -of -Distribution detection to estimate different uncertainties for the acquisition function. Specifically, the weighted epistemic uncertainty accounts for the class imbalance, aleatoric uncertainty for ambiguous images, and an OoD score for artifacts. We perform extensive experiments to validate our method on MNIST and the real -world Panda dataset for the classification of prostate cancer. The results confirm that other AL methods are 'distracted' by ambiguities and artifacts which harm the performance. FocAL effectively focuses on the most informative images, avoiding ambiguities and artifacts during acquisition. For both experiments, FocAL outperforms existing AL approaches, reaching a Cohen's kappa of 0.764 with only 0.69% of the labeled Panda data.
Keyword:
Active
learning Cancer classification
Histopathological images
Bayesian deep learning
期刊
IF:
11.8
论文数:
3.9K
被引数:
2.4W
机构
引用论文
The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge前列腺癌诊断和Gleason分级的人工智能: 熊猫挑战NATURE MEDICINE
OAAI
NATURE MEDICINEIF50
OAAI分享收藏Clinical-grade computational pathology using weakly supervised deep learning on whole slide images在整个幻灯片图像上使用弱监督深度学习的临床级计算病理学NATURE MEDICINEOAAI
NATURE MEDICINEIF50
OAAI分享收藏分享收藏Interactive Classification of Whole-Slide Imaging Data for Cancer ResearchersCANCER RESEARCHOAAI
CANCER RESEARCHIF16.6
OAAI分享收藏Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning结合半监督和多示例学习的高效癌症分类IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容