Abstract
Computational pathology tasks have some unique characterises such as multi-gigapixel images, tedious and frequently uncertain annotations, and unavailability of large number of cases [13]. To address some of these issues, we present Deep Fastfood Ensembles - a simple, fast and yet effective method for combining deep features pooled from popular CNN models pre-trained on totally different source domains (e.g., natural image objects) and projected onto diverse dimensions using random projections, the so-called Fastfood [11]. The final ensemble output is obtained by a consensus of simple individual classifiers, each of which is trained on a different collection of random basis vectors. This offers extremely fast and yet effective solution, especially when training times and domain labels are of the essence. We demonstrate the effectiveness of the proposed deep fastfood ensemble learning as compared to the state-of-the-art methods for three different tasks in histopathology image analysis.
Abstract (translated)
计算病理诊断任务有一些独特的特征,例如涉及大量像素的图像、繁琐且常常不确定的标注、以及缺乏大量案例的情况 [13]. 为了解决这些问题,我们提出了Deep Fastfood Ensembles方法 - 一种简单、快速但有效的方法,将来自通用卷积神经网络模型在完全不同的源 domains(例如自然图像对象)上预训练的深特征合并起来,并通过随机投影将其投影到多个维度上,从而产生所谓的 Fastfood(11)。最终 ensemble output 由简单的个体分类器的共识得出,每个分类器都训练在一个不同的随机向量集合上。这种方法提供了极快但有效的解决方案,特别是在训练时间和域标签是至关重要的情况下。我们证明了 proposed Deep Fastfood Ensemble Learning方法相对于先进的病理图像分析三个任务方法的 effectiveness。
URL
https://arxiv.org/abs/2301.09525
