CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification

• URL: http://arxiv.org/abs/2004.12064v2
• Date: Wed, 9 Sep 2020 04:37:03 GMT
• Title: CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification
• Authors: Di Zhuang, Keyu Chen, J. Morris Chang
• Abstract summary: Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. We present CS-AF, a cost-sensitive multi-classifier active fusion framework for skin lesion classification.
• Score: 9.265557367859637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. Compared with single CNN classifier, combining the results of multiple classifiers via fusion approaches shows to be more effective and robust. Since the skin lesion datasets are usually limited and statistically biased, while designing an effective fusion approach, it is important to consider not only the performance of each classifier on the training/validation dataset, but also the relative discriminative power (e.g., confidence) of each classifier regarding an individual sample in the testing phase, which calls for an active fusion approach. Furthermore, in skin lesion analysis, the data of certain classes (e.g., the benign lesions) is usually abundant making them an over-represented majority, while the data of some other classes (e.g., the cancerous lesions) is deficient, making them an underrepresented minority. It is more crucial to precisely identify the samples from an underrepresented (i.e., in terms of the amount of data) but more important minority class (e.g., certain cancerous lesion). In other words, misclassifying a more severe lesion to a benign or less severe lesion should have relative more cost (e.g., money, time and even lives). To address such challenges, we present CS-AF, a cost-sensitive multi-classifier active fusion framework for skin lesion classification. In the experimental evaluation, we prepared 96 base classifiers (of 12 CNN architectures) on the ISIC research datasets. Our experimental results show that our framework consistently outperforms the static fusion competitors.

Related papers

• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images [7.159532626507458]
  This study introduces a Graph Convolution Network (GCN) to exploit prior co-occurrence between each category as a correlation matrix into the deep learning model for the multi-label classification. We propose a Graph-Ensemble Learning Model (GELN) that views the prediction from GCN as complementary information of the predictions from the fusion model.
  arXiv  Detail & Related papers  (2023-07-04T13:19:57Z)
• Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets [53.34152466646884]
  In this paper, we show how bringing recent results on equivariant representation learning instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. We demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples.
  arXiv  Detail & Related papers  (2022-03-29T04:54:06Z)
• SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification [9.265557367859637]
  SuperCon is a two-stage training strategy to overcome the class imbalance problem on skin lesion classification. Our two-stage training strategy effectively addresses the class imbalance classification problem, and significantly improves existing works in terms of F1-score and AUC score.
  arXiv  Detail & Related papers  (2022-02-11T15:19:36Z)
• On the Fairness of Swarm Learning in Skin Lesion Classification [22.896631007125244]
  Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous, and distributed data sources. We present an empirical study by comparing the fairness among single (node) training, SL, centralized training. Experiments demonstrate that SL does not exacerbate the fairness problem compared to centralized training.
  arXiv  Detail & Related papers  (2021-09-24T20:20:24Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• 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)
• Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification [5.642359877598896]
  This paper proposes a novel data augmentation strategy for single model classification of skin lesions based on a small and imbalanced dataset. Various DCNNs are trained on this dataset to show that the models with moderate complexity outperform the larger models. By combining Modified RandAugment and Multi-weighted Focal Loss in a single DCNN model, we have achieved the classification accuracy comparable to those of multiple ensembling models on the ISIC 2018 challenge test dataset.
  arXiv  Detail & Related papers  (2021-02-02T03:48:55Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.