Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018

Bissoto, Alceu; Perez, Fábio; Ribeiro, Vinícius de Oliveira; Fornaciali, Michel; Avila, Sandra; Valle, Eduardo

doi:10.48550/arxiv.1808.08480

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…An increasing number of skin lesion segmentation methods with deep learning have emerged. Bissoto et al (2018) utilized U-Net to segment skin lesions. They evaluated the model on the ISIC2018 dataset and got an intersection over Union (IoU) score of 72.8%.…”

Section: Skin Lesion Segmentationmentioning

confidence: 99%

JAAL-Net: a joint attention and adversarial learning network for skin lesion segmentation

Xiong¹,

Pan²,

Lei³

et al. 2023

Phys. Med. Biol.

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Objective. Skin lesion segmentation plays an important role in the diagnosis and treatment of melanoma. Existing skin lesion segmentation methods have trouble distinguishing hairs, air bubbles, and blood vessels around lesions, which affects the segmentation performance. Approach. To clarify the lesion boundary and raise the accuracy of skin lesion segmentation, a joint attention and adversarial learning network (JAAL-Net) is proposed that consists of a generator and a discriminator. In the JAAL-Net, the generator is a Local Fusion Network (LF-Net) utilizing the encoder-decoder structure. The encoder contains a convolutional block attention module to increase the weight of lesion information. The decoder involves a contour attention to obtain edge information and locate the lesion. To aid the LF-Net generate higher confidence predictions, a discriminant Dual Attention Network (DA-Net) is constructed with channel attention and position attention. Main results. The JAAL-Net is evaluated on three datasets ISBI2016, ISBI2017 and ISIC2018. The intersection over union (IoU) of the JAAL-Net on the three datasets are 90.27%, 89.56% and 80.76%, respectively. Experimental results show that the JAAL-Net obtains rich lesion and boundary information, enhances the confidence of the predictions, and improves the accuracy of skin lesion segmentation. Significance. The proposed approach effectively improves the performance of the model for skin lesion segmentation, which can assist physicians in accurate diagnosis well.

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Section: Skin Lesion Segmentationmentioning

confidence: 99%

JAAL-Net: a joint attention and adversarial learning network for skin lesion segmentation

Xiong¹,

Pan²,

Lei³

et al. 2023

Phys. Med. Biol.

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“…These sources include images and their corresponding skin condition label. While these labels are not known to be confirmed by a biopsy, these images and their skin condition labels have been used and cited in dermatology and computer vision literature a number of times [23,29,9,45,6,50,53]. As a data quality check, we asked a board-certified dermatologist to evaluate the diagnostic accuracy of 3% of the dataset.…”

Section: Fitzpatrick 17k Datasetmentioning

confidence: 99%

Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset

Groh¹,

Harris²,

Soenksen³

et al. 2021

Preprint

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How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision models can serve as a useful decision support tool in healthcare and provide dermatologist-level classification on a number of specific tasks, darker skin is underrepresented in the data. Most publicly available data sets do not include Fitzpatrick skin type labels. We annotate 16,577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels and open-source these annotations. Based on these labels, we find that there are significantly more images of light skin types than dark skin types in this dataset. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on. In addition, we evaluate how an algorithmic approach to identifying skin tones, individual typology angle, compares with Fitzpatrick skin type labels annotated by a team of human labelers.

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“…Bissoto et al [44] used a UNet-like model with its encoding path pre-trained on ImageNet data set. They also used this architecture for lesion segmentation in which they incorporated external data in their training procedures besides the original ISIC2018 challenge data set.…”

Section: B Attribute Segmentationmentioning

confidence: 99%

Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations

Jahanifar,

Tajeddin,

Koohbanani

et al. 2018

Preprint

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Computer-aided diagnosis systems for classification of different type of skin lesions have been an active field of research in recent decades. It has been shown that introducing lesions and their attributes masks into lesion classification pipeline can greatly improve the performance. In this paper, we propose a framework by incorporating transfer learning for segmenting lesions and their attributes based on the convolutional neural networks. The proposed framework is based on the encoderdecoder architecture which utilizes a variety of pre-trained networks in the encoding path and generates the prediction map by combining multi-scale information in decoding path using a pyramid pooling manner. To address the lack of training data and increase the proposed model generalization, an extensive set of novel domain-specific augmentation routines have been applied to simulate the real variations in dermoscopy images. Finally, by performing broad experiments on three different data sets obtained from International Skin Imaging Collaboration archive (ISIC2016, ISIC2017, and ISIC2018 challenges data sets), we show that the proposed method outperforms other state-of-the-art approaches for ISIC2016 and ISIC2017 segmentation task and achieved the 1 st rank on the leader-board of ISIC2018 attribute detection task.

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Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018

Cited by 7 publications

References 15 publications

JAAL-Net: a joint attention and adversarial learning network for skin lesion segmentation

JAAL-Net: a joint attention and adversarial learning network for skin lesion segmentation

Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset

Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations

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