Abstract
Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.
Highlights
Medical images play a very crucial role in patient treatment; usually, the shortage of manpower, the time required to reach a decision, and the need for a second opinion are factors that greatly impact the process
In the United States, the number of active pathologists dropped 17.5% in the last decade while the workload increased by 41% [2], which indicates a real need for assisting the pathologists in their work by providing them with an autonomous classifier that is able to classify histopathology images with a high level of accuracy
The second block was the 4th, which was fine-tuned with the 5th layer, and the highest area under the curve (AUC) obtained was by the 10−4 learning rate followed by the 10−3 and 10−5
Summary
Medical images play a very crucial role in patient treatment; usually, the shortage of manpower, the time required to reach a decision, and the need for a second opinion are factors that greatly impact the process. Histopathology images are very important for detecting certain kinds of diseases like cancer or even determining the kind of cancer itself to see if it is benign or malignant and its degree. Histopathology is defined as examining a tissue sample taken by a biopsy to diagnose certain diseases microscopically [1]. It plays a very important role in the detection of diseases, enabling doctors to carefully create a treatment plan. In the United States, the number of active pathologists dropped 17.5% in the last decade while the workload increased by 41% [2], which indicates a real need for assisting the pathologists in their work by providing them with an autonomous classifier that is able to classify histopathology images with a high level of accuracy
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