Image Analysis and Machine Learning in Cancers

Dear Colleagues,

Over the years, we have seen a tremendous advance in image analysis and machine learning (ML) techniques for cancer detection. This phenomenon has been powered mainly by better equipment capturing data with higher quality, availability of public datasets, and the advances of computer technology that enable us to use methods that years ago were not possible. For example, we have seen many papers dealing with super-resolution images, fusing images from different modalities towards a better diagnosis and even generating more data based on the limited amount available.

Imaging processing techniques are the basis of all Artificial Intelligence (AI)-based systems. It has been proven that pre-processing methods cause a huge impact on the next steps in an ML/AI pipeline. Therefore, it is common to see many papers proposing new methods to enhance images even further towards a better result.

Machine learning approaches, also known as conventional approaches, have been essential in pushing the boundaries in the detection and diagnosis of cancers. Since they can work with limited datasets and more modest computers, as opposed to the deep learning approaches, many methods have been proposed since the popularization of AI. Nowadays, these methods compete equally with the DL-based ones in terms of accuracy, specificity, and sensitivity.

Deep learning (DL) techniques, followed by the advances and accessibility of more powerful hardware, have played an important role in this scenario. Since its basis lies in imaging analysis and machine learning, recent advances have shown the many possibilities to provide a good diagnosis reducing the follow-ups of patients. However, DL models are known for their lack of explainability, although some studies have proposed ways to overcome this.

This Special Issue is dedicated to covering the most recent advances in image analysis and machine learning techniques toward a better detection/diagnosis of cancers in general.

Dr. Helder C. R. De Oliveira
Dr. Arianna Mencattini
Guest Editors

Manuscript Submission Information

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