Hair removal in dermoscopy images using variational autoencoders
TL;DR: Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin and it is important to eliminate the hair to get accurate results.
read more
Abstract: In recent years, melanoma is rising at a faster rate compared to other cancers. Although it is the most serious type of skin cancer, the diagnosis at early stages makes it curable. Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin. In the last few decades, digital imaging devices have made great progress which allowed capturing and storing high‐quality images from these examinations. The stored images are now being standardized and used for the automatic detection of melanoma. However, when the hair covers the skin, this makes the task challenging. Therefore, it is important to eliminate the hair to get accurate results.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Hair removal in dermoscopy images using variational autoencoders
TL;DR: Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin and it is important to eliminate the hair to get accurate results.
26
EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images
Daniel Addo,Shijie Zhou,Jehoiada Jackson,Grace U. Nneji,Happy N. Monday,K. Sarpong,Rutherford Agbeshi Patamia,Favour Ekong,Christyn Akosua Owusu-Agyei +8 more
TL;DR: This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease.
Detection of melanoma with hybrid learning method by removing hair from dermoscopic images using image processing techniques and wavelet transform
TL;DR: In this paper , a hybrid model combining deep learning and machine learning has been used for melanoma detection, which achieved the best accuracy of 99.44% and 100% in the literature.
10
EBAT: Enhanced Bidirectional and Autoregressive Transformers for Removing Hairs in Hairy Dermoscopic Images
01 Jan 2023
TL;DR: In this paper , a generative image inpainting network where bidirectional autoregressive transformers (BATs) are employed to learn image features and are systematically integrated with convolutional neural networks (CNNs) in multiple spatial scales in order to reconstruct missing regions.
5
Deep learning-based hair removal for improved diagnostics of skin diseases
Walid El-Shafai,Ibrahim Abd El-Fattah,Taha E. Taha +2 more
4
References
Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.
TL;DR: Advances in the digital image-based AI solutions for the diagnosis of skin cancer are discussed, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer.
313
A comprehensive evaluation of full reference image quality assessment algorithms
Lin Zhang,Lei Zhang,Xuanqin Mou,David Zhang +3 more
- 01 Sep 2012
TL;DR: This paper reports the performance of eleven selected FR IQA algorithms on all the seven public IQA image datasets and hopes that the evaluation results and the associated discussions will be very helpful for relevant researchers to have a clearer understanding about the status of modernFR IQA indices.
Deep Generative Adversarial Compression Artifact Removal
Leonardo Galteri,Lorenzo Seidenari,Marco Bertini,Alberto Del Bimbo +3 more
- 01 Oct 2017
TL;DR: In this article, a generative adversarial network (GAN) is proposed to produce more photorealistic details than MSE or SSIM-based networks, which can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail.
214
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
Mohammad Saeed Rad,Behzad Bozorgtabar,Urs-Viktor Marti,Max Basler,Hazim Kemal Ekenel,Jean-Philippe Thiran +5 more
- 01 Oct 2019
TL;DR: In this paper, the authors optimize a deep network-based decoder with a targeted objective function that penalizes images at different semantic levels using the corresponding terms, which results in more realistic textures and sharper edges.
Dermoscopy Image Analysis: Overview and Future Directions
TL;DR: A brief overview of this exciting subfield of medical image analysis, primarily focusing on three aspects of it, namely, segmentation, feature extraction, and classification is presented.
182