A survey on deep learning in medical image analysis

https://hal.inria.fr/hal-01648559/document

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.

Deep Learning in Medical Ultrasound Analysis: A Review

In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

Deep Learning and Medical Diagnosis: A Review of Literature

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

Deep learning based classification of breast tumors with shear-wave elastography.

Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy with computer-extracted features from contrast-enhanced ultrasound videos.

Speckle noise contaminates medical ultrasound images and the suppression of speckle noise is valuable for image interpretation. This paper presents a new method for speckle suppression by integrating the non-local means (NLM) with the McIlhagga-based anisotropic diffusion (MAD). The MAD is first used to get a diffused image from the initial noisy image, and then the NLM is conducted to get a final denoised image by processing both the initial image and the diffused image. Experimental results on both simulated and real ultrasound images validate the feasibility of the method and demonstrate its superiority to seven state-of-the-art methods in terms of noise reduction and detail preservation.

Wei Dai

Papers

Deep learning based classification of breast tumors with shear-wave elastography.

Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy with computer-extracted features from contrast-enhanced ultrasound videos.

Speckle suppression of ultrasonic images using non-local means and McIlhagga-based anisotropic diffusion