AI Progress in Skin Lesion Analysis

• URL: http://arxiv.org/abs/2009.13323v2
• Date: Fri, 9 Oct 2020 16:58:15 GMT
• Title: AI Progress in Skin Lesion Analysis
• Authors: Philippe M. Burlina, William Paul, Phil A. Mathew, Neil J. Joshi, Alison W. Rebman, John N. Aucott
• Abstract summary: Problems of AI bias regarding the lack of skin images in dark individuals, being able to accurately detect, delineate, and segment lesions or regions of interest, and low shot learning. We report skin analysis algorithms that gracefully degrade and still perform well at low shots, when compared to baseline algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We examine progress in the use of AI for detecting skin lesions, with particular emphasis on the erythema migrans rash of acute Lyme disease, and other lesions, such as those from conditions like herpes zoster (shingles), tinea corporis, erythema multiforme, cellulitis, insect bites, or tick bites. We discuss important challenges for these applications, in particular the problems of AI bias regarding the lack of skin images in dark skinned individuals, being able to accurately detect, delineate, and segment lesions or regions of interest compared to normal skin in images, and low shot learning (addressing classification with a paucity of training images). Solving these problems ranges from being highly desirable requirements -- e.g. for delineation, which may be useful to disambiguate between similar types of lesions, and perform improved diagnostics -- or required, as is the case for AI de-biasing, to allow for the deployment of fair AI techniques in the clinic for skin lesion analysis. For the problem of low shot learning in particular, we report skin analysis algorithms that gracefully degrade and still perform well at low shots, when compared to baseline algorithms: when using a little as 10 training exemplars per class, the baseline DL algorithm performance significantly degrades, with accuracy of 56.41%, close to chance, whereas the best performing low shot algorithm yields an accuracy of 85.26%.

Related papers

• Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset [4.391219238034502]
  Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. We propose a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention.
  arXiv  Detail & Related papers  (2024-08-24T10:14:52Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings [17.345850219146424]
  We show that differences in skin condition distribution are the main source of errors when an AI algorithm is evaluated on data from a previously unseen source. Our results suggest comparable performance from end-to-end fine tuning versus fine tuning solely the classification layer on top of a frozen embedding model.
  arXiv  Detail & Related papers  (2024-02-23T19:07:53Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• Convolutional Neural Networks Towards Facial Skin Lesions Detection [0.0]
  This study contributes by providing a model that facilitates the detection of blemishes and skin lesions on facial images. The proposed method offers advantages such as simple architecture, speed and suitability for image processing.
  arXiv  Detail & Related papers  (2024-02-13T16:52:10Z)
• Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection [0.0]
  Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection.
  arXiv  Detail & Related papers  (2023-09-02T04:42:08Z)
• Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis [7.371818587876888]
  This research focuses on a two step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effectiveness of deep convolutional neural network based architectures by designing an encoder-decoder architecture for skin lesion segmentation and CNN based classification network. Our cascaded end to end deep learning based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation.
  arXiv  Detail & Related papers  (2023-01-16T01:08:32Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification [76.89093364969253]
  We propose to apply Few-Shot Learning to skin disease identification to address the extreme scarcity of training sample problem. Based on a detailed analysis, we propose the Query-Relative (QR) loss, which proves superior to Cross Entropy (CE) under episode training. We further strengthen the proposed QR loss with a novel adaptive hard margin strategy.
  arXiv  Detail & Related papers  (2020-04-21T00:57:11Z)

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.