Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks
This addresses the need for more accurate automated diagnosis of breast tumor malignancy, though it is incremental as it builds on existing CNN methods by incorporating multiple magnification levels.
The paper tackled the problem of histopathological malignancy diagnosis by proposing a case-based approach using deep residual neural networks that processes images at multiple magnification levels, achieving better performance than state-of-the-art methods on the BreaKHis dataset.
In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined. However, previous research on such classification tasks using convolutional neural networks primarily determine a diagnosis for a single magnification level. In this paper, we propose a case-based approach using deep residual neural networks for histopathological malignancy diagnosis, where a case is defined as a sequence of images from the patient at all available levels of magnification. Effectively, through mimicking what a human expert would actually do, our approach makes a diagnosis decision based on features learned in combination at multiple magnification levels. Our results show that the case-based approach achieves better performance than the state-of-the-art methods when evaluated on BreaKHis, a histopathological image dataset for breast tumors.