Melanoma Recognition via Visual Attention

Yan, Yiqi; Kawahara, Jeremy; Hamarneh, Ghassan

doi:10.1007/978-3-030-20351-1_62

Cited by 54 publications

(64 citation statements)

References 16 publications

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“…In References [ 11 , 12 ], ensemble methods were used to fuse the predictions of different classifiers, improving accuracy. Further, the information of image segmentation was utilized in the modeling process to mitigate the negative influence of the background [ 13 , 14 , 15 ]. As such, the large intra-class variations and high inter-class similarity can be suppressed to a certain extent.…”

Section: Related Workmentioning

confidence: 99%

“…In Reference [ 13 ], a two-stage method was proposed to segment the skin lesions before recognition. Yan et al [ 14 ] used the segmentation information to regularize attention modules, focusing on the discriminative regions. In Reference [ 15 ], region average pooling was utilized to highlight relevant areas with the score map of image segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Nonetheless, the manual inspection for dermoscopy images is subjective and experimental. Numerous computer-aided diagnosis methods have been presented to perform melanoma recognition [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, deep convolutional neural networks (DCNNs) have demonstrated the excellent feature representation ability in the computer vision field. Various methods [ 2 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] adopted DCNNs for melanoma recognition. Owing to the strong representation ability, the DCNN-based methods can generally perform the classification task effectively.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss

Guo

Xie

et al. 2020

Sensors

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Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss

Guo

Xie

et al. 2020

Sensors

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show abstract

“…However, these methods use hand-crafted features, and therefore rely on an accurate segmentation of the lesion [2]. Moreover, lesion segmentations have been used to assist melanoma diagnosis [10,21,24]. This motivates the use of deep learning based computer-aided diagnosis systems to improve the accuracy and sensitivity of melanoma detection methods.…”

Section: Introductionmentioning

confidence: 99%

Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis

Abhishek

Hamarneh

2019

Lecture Notes in Computer Science

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Skin lesion segmentation is a vital task in skin cancer diagnosis and further treatment. Although deep learning based approaches have significantly improved the segmentation accuracy, these algorithms are still reliant on having a large enough dataset in order to achieve adequate results. Inspired by the immense success of generative adversarial networks (GANs), we propose a GAN-based augmentation of the original dataset in order to improve the segmentation performance. In particular, we use the segmentation masks available in the training dataset to train the Mask2Lesion model, and use the model to generate new lesion images given any arbitrary mask, which are then used to augment the original training dataset. We test Mask2Lesion augmentation on the ISBI ISIC 2017 Skin Lesion Segmentation Challenge dataset and achieve an improvement of 5.17% in the mean Dice score as compared to a model trained with only classical data augmentation techniques.

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Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma

Bokadia

Yang

et al. 2022

Applied AI Letters

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In order to be useful, XAI explanations have to be faithful to the AI system they seek to elucidate and also interpretable to the people that engage with them. There exist multiple algorithmic methods for assessing faithfulness, but this is not so for interpretability, which is typically only assessed through expensive user studies. Here we propose two complementary metrics to algorithmically evaluate the interpretability of saliency map explanations. One metric assesses perceptual interpretability by quantifying the visual coherence of the saliency map. The second metric assesses semantic interpretability by capturing the degree of overlap between the saliency map and textbook features-features human experts use to make a classification. We use a melanoma dataset and a deep-neural network classifier as a case-study to explore how our two interpretability metrics relate to each other and a faithfulness metric. Across six commonly used saliency methods, we find that none achieves high scores across all three metrics for all test images, but that different methods perform well in different regions of the data distribution. This variation between methods can be leveraged to consistently achieve high interpretability and faithfulness by using our metrics to inform saliency mask selection on a case-by-case basis. Our interpretability metrics provide a new way to evaluate saliency-based explanations and allow for the adaptive combination of saliency-based explanation methods.

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Melanoma Recognition via Visual Attention

Cited by 54 publications

References 16 publications

Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss

Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss

Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis

Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma

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