Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation

• URL: http://arxiv.org/abs/2011.00148v1
• Date: Sat, 31 Oct 2020 00:16:23 GMT
• Title: Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation
• Authors: Anindo Saha, Prem Prasad, Abdullah Thabit
• Abstract summary: We propose an adaptive color augmentation technique to amplify data expression and model performance. We qualitatively identify and verify the semantic structural features learned by the network for discriminating skin lesions against normal skin tissue.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fully automatic detection of skin lesions in dermatoscopic images can facilitate early diagnosis and repression of malignant melanoma and non-melanoma skin cancer. Although convolutional neural networks are a powerful solution, they are limited by the illumination spectrum of annotated dermatoscopic screening images, where color is an important discriminative feature. In this paper, we propose an adaptive color augmentation technique to amplify data expression and model performance, while regulating color difference and saturation to minimize the risks of using synthetic data. Through deep visualization, we qualitatively identify and verify the semantic structural features learned by the network for discriminating skin lesions against normal skin tissue. The overall system achieves a Dice Ratio of 0.891 with 0.943 sensitivity and 0.932 specificity on the ISIC 2018 Testing Set for segmentation.

Related papers

• FairSkin: Fair Diffusion for Skin Disease Image Generation [54.29840149709033]
  Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers from a critical twofold bias. We propose FairSkin, a novel DM framework that mitigates these biases through a three-level resampling mechanism. Our approach significantly improves the diversity and quality of generated images, contributing to more equitable skin disease detection in clinical settings.
  arXiv  Detail & Related papers  (2024-10-29T21:37:03Z)
• LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation [8.566930077350184]
  We propose a novel lightweight network specifically designed for skin lesion segmentation utilizing mobile devices. Our network comprises an encoder-decoder architecture that incorporates conformer-based focal modulation attention, self-aware local and global spatial attention, and split channel-shuffle. Empirical findings substantiate its state-of-the-art performance, notably reflected in a high Jaccard index.
  arXiv  Detail & Related papers  (2024-09-03T03:06:32Z)
• DDI-CoCo: A Dataset For Understanding The Effect Of Color Contrast In Machine-Assisted Skin Disease Detection [51.92255321684027]
  We study the interaction between skin tone and color difference effects and suggest that color difference can be an additional reason behind model performance bias between skin tones. Our work provides a complementary angle to dermatology AI for improving skin disease detection.
  arXiv  Detail & Related papers  (2024-01-24T07:45:24Z)
• Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs [59.19218582436495]
  We propose an unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED. Skin lesions are segmented by minimizing the structural entropy of a superpixel graph constructed from the dermoscopic image. We characterize the consistency of healthy skin features and devise a novel multi-scale segmentation mechanism by outlier detection, which enhances the segmentation accuracy by leveraging the superpixel features from multiple scales.
  arXiv  Detail & Related papers  (2023-09-05T02:15:51Z)
• Data-Driven Deep Supervision for Skin Lesion Classification [36.24996525103533]
  We propose a new deep neural network that exploits input data for robust feature extraction. Specifically, we analyze the convolutional network's behavior (field-of-view) to find the location of deep supervision.
  arXiv  Detail & Related papers  (2022-09-04T03:57:08Z)
• Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network [28.709314434820953]
  Current skin lesion segmentation approaches show poor performance in challenging circumstances. We propose a dilated scale-wise feature fusion network based on convolution factorization. Our proposed model consistently showcases state-of-the-art results.
  arXiv  Detail & Related papers  (2022-05-20T16:08:37Z)
• EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation [9.750368551427494]
  Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. These models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones.
  arXiv  Detail & Related papers  (2022-02-28T15:33:31Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification [5.71097144710995]
  We use a modified variational autoencoder to uncover skin tone bias in datasets commonly used as benchmarks. We propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images. We subsequently use two leading bias unlearning techniques to mitigate skin tone bias.
  arXiv  Detail & Related papers  (2022-02-06T18:53:06Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.