DeepFEL: Deep Fastfood Ensemble Learning for Histopathology Image Analysis
This work addresses computational pathology problems for medical researchers, offering a fast and effective solution, though it appears incremental as it builds on existing techniques like Fastfood and ensemble learning.
The paper tackled computational pathology challenges like large images and limited annotations by proposing Deep Fastfood Ensembles, a method combining deep features from pre-trained CNNs with random projections, achieving effective results in histopathology image analysis across three tasks compared to state-of-the-art methods.
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.