Data augmentation for automated melanoma lesion detection

Training data balance is crucial to the performance of machine learning (ML) models, especially deep learning models. There would be a high risk of overfitting when training on unbalanced datasets.

Image Inpainting

Image inpainting is the technique of reconstructing of missing portions in an image in an undetectable way, restoring both texture and structure.

Gender equity in artificial intelligence applications in dermatology

In the last few years, there have been remarkable developments in computational methods for helping dermatologists to diagnose skin cancer in early stages.

Image Colour Transfer

Image colour transfer aims to alter the colours of one image (source) to mimic the appearance and colour palette of another one (target).

Image segmentation for dermoscopy

In this blogpost we will talk about the importance of image segmentation for dermoscopy as well as other medical imaging techniques. For skin cancer screening it is of importance to segment the nevus from the skin.

Classification system for skin lesions, the more detailed the better

Skin cancer is one of the most common cancers in the world. Late-stage skin cancers spreads to internal organs and become fatal. Early-stage skin cancer can be cured with a high survival rate, while the 5-year survival rate for skin cancer is extremely low. Therefore, early detection is the key in fighting skin cancer.

Image Super-Resolution

Image Super-Resolution is the task of restoring a high-resolution image out of one or several lower resolution images. A simple enlargement of the image would produce a blurred result, so a special approach is needed to boost the apparent resolution and enhance image sharpness.

Image Deblurring

Image deblurring is the technique of removing blurring artifacts from an image that can come from object motion, camera shake or out-of-focus blur.

An introduction to image denoising

Image acquisition comes with unavoidable and unwanted noise acquisition due to camera hardware limitations and illumination challenges, making image denoising a fundamental task in image processing.

Efficient AI Predictions through Explainability-driven Neural Network Quantization

Solving increasingly complex real-world problems, continuously contributes to the success of deep neural networks (DNNs) (Schütt et al. 2017; Senior et al. 2020). DNNs have long been established in numerous machine learning tasks and for this have been significantly improved in the past decade.