Recent trends in histopathological image analysis

Histopathological images provide a plethora of phenotypic information that forms the basis for proven to be the gold standard for cancer diagnosis and monitoring the progression of the disease in cancer patients. However, such images are challenging to analyse, even for experienced pathologists. Moreover, manual analysis is a tedious and costly task in terms of labour, time, etc. The manual analysis is also affected by intra- and inter-observer disagreement, as reported in several studies. Therefore, computer-aided diagnosis (CAD) systems are being explored to speed up the analysis process. Nowadays, artificial intelligence (AI)-based solutions are quite popular in the medical domain, and deep learning (DL) is becoming the most popular methodological choice for researchers to analyse histopathological images. Usually, feature extraction, image segmentation and histopathological image classification are the popular tasks for which several machine learning (ML) approaches and deep models have been developed. There are also few works that are designed for Internet-of-Things-based applications while also addressing security concerns. Therefore, this chapter briefly presents the recent developments in the automated histopathological analysis of cancer. We further summarise different publicly available datasets and also emphasise the key challenges along with limitations of emerging DL techniques for CAD of cancer. We also provide an insight into possible avenues for future research in this area. It helps the researchers working in this area to leverage the opportunities and challenges that direct towards innovative developments in the field.