Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
arxiv(2024)
摘要
Histopathology is a gold standard for cancer diagnosis under a microscopic
examination. However, histological tissue processing procedures result in
artifacts, which are ultimately transferred to the digitized version of glass
slides, known as whole slide images (WSIs). Artifacts are diagnostically
irrelevant areas and may result in wrong deep learning (DL) algorithms
predictions. Therefore, detecting and excluding artifacts in the computational
pathology (CPATH) system is essential for reliable automated diagnosis. In this
paper, we propose a mixture of experts (MoE) scheme for detecting five notable
artifacts, including damaged tissue, blur, folded tissue, air bubbles, and
histologically irrelevant blood from WSIs. First, we train independent binary
DL models as experts to capture particular artifact morphology. Then, we
ensemble their predictions using a fusion mechanism. We apply probabilistic
thresholding over the final probability distribution to improve the sensitivity
of the MoE. We developed DL pipelines using two MoEs and two multiclass models
of state-of-the-art deep convolutional neural networks (DCNNs) and vision
transformers (ViTs). DCNNs-based MoE and ViTs-based MoE schemes outperformed
simpler multiclass models and were tested on datasets from different hospitals
and cancer types, where MoE using DCNNs yielded the best results. The proposed
MoE yields 86.15
less computational cost for inference than MoE using ViTs. This best
performance of MoEs comes with relatively higher computational trade-offs than
multiclass models. The proposed artifact detection pipeline will not only
ensure reliable CPATH predictions but may also provide quality control.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要