Advances in Deep Learning-Based Medical Image Analysis

Dear Colleagues,

Deep learning is the prominent research direction for medical image analysis. The hierarchical nature of deep models learns the complex patterns in medical images, facilitating image-based diagnostics and prognosis. Different imaging modalities, including not but not limited to RGB, CT, MRI, X-ray, ultrasound, PETS, EEG, and mammogram, are used for inferring valuable insights about the patient’s medical condition. In addition, multi-modality- and cross-modality-based learning algorithms are also explored where the models are learned using more than a single imaging modality.

In the last decade, many algorithms have been proposed, from cell segmentation to anomaly detection, with the aim of aiding radiologists and medical doctors. However, there are many limiting factors that create barriers to the ubiquitous application of such techniques in clinical practices. The availability of a large amount of high-quality labeled data, the real-time performance bottleneck, and the accuracy of algorithms themselves are some of the key challenges that researchers are currently faced with.

Irrespective of the type of data modality, this Special Issue of Applied sciences titled “Advance in Deep Learning-Based Medical Image Analysis” is dedicated to covering recent advancements in this domain.

Dr. Mohib Ullah
Guest Editor

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