Robust Skin Disease Classification by Distilling Deep Neural Network Ensemble for the Mobile Diagnosis of Herpes Zoster

Back, Seunghyeok; Lee, Seongju; Shin, Sungho; Yu, Yeonguk; Yuk, Taekyeong; Jong, Saepomi; Ryu, Seungjun; Lee, Kyoobin

doi:10.1109/access.2021.3054403

Cited by 31 publications

(8 citation statements)

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“…Recently, deep learning has been widely applied in the medical field [27][28][29][30]. In particular, deep learning demonstrates high performance in image classification, segmentation, and detection.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, deep learning is applied to data classification, segmentation, and detection in various domains, such as images and signals [20][21][22][23][24][25][26]. In particular, deep learning demonstrates high performance in medical imaging [27][28][29][30]. Deep learning is also used to assist with various tasks, including caries detection and third molar extraction, in dentistry [14,15,31,32].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Detection of Missing Tooth Regions for Dental Implant Planning in Panoramic Radiographic Images

et al. 2022

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Dental implantation is a surgical procedure in oral and maxillofacial surgery. Detecting missing tooth regions is essential for planning dental implant placement. This study proposes an automated method that detects regions of missing teeth in panoramic radiographic images. Tooth instance segmentation is required to accurately detect a missing tooth region in panoramic radiographic images containing obstacles, such as dental appliances or restoration. Therefore, we constructed a dataset that contains 455 panoramic radiographic images and annotations for tooth instance segmentation and missing tooth region detection. First, the segmentation model segments teeth into the panoramic radiographic image and generates teeth masks. Second, a detection model uses the teeth masks as input to predict regions of missing teeth. Finally, the detection model identifies the position and number of missing teeth in the panoramic radiographic image. We achieved 92.14% mean Average Precision (mAP) for tooth instance segmentation and 59.09% mAP for missing tooth regions detection. As a result, this method assists diagnosis by clinicians to detect missing teeth regions for implant placement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Detection of Missing Tooth Regions for Dental Implant Planning in Panoramic Radiographic Images

et al. 2022

Self Cite

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“…The findings of the experiments were revealed in order to gather remote knowledge and enhance global accuracy; some local accuracy was lost. To improve the robustness and reduce the computational cost of the model ( 115 ), proposed a knowledge distillation method based on curriculum training in distinguishing herpes zoster from other skin diseases. Firstly, three kinds of model, namely, basic models, mobile models, and ensemble models, were chosen for benchmark.…”

Section: Methods For Typical and Frontier Problems In Skin Cancer Cla...mentioning

confidence: 99%

Skin Cancer Classification With Deep Learning: A Systematic Review

Chen

Zeng

et al. 2022

Front. Oncol.

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Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

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“…They exploit Self-paced Expert Selection and Curriculum Instance Selection as learning schedules for a reliable knowledge transfer from Experts to the student networks. Various works take advantage of KD with multiple teachers in different applications of computer vision, such as person re-identification [56], skin disease classification [57], and video action recognition [58].…”

Section: B Multi-teacher Kdmentioning

confidence: 99%

Robust Semantic Segmentation With Multi-Teacher Knowledge Distillation

Amirkhani¹,

Khosravian

Masih‐Tehrani

et al. 2021

IEEE Access

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Recent studies have recently exploited knowledge distillation (KD) technique to address timeconsuming annotation task in semantic segmentation, through which one teacher trained on a single dataset could be leveraged for annotating unlabeled data. However, in this context, knowledge capacity is restricted, and knowledge variety is rare in different conditions, such as cross-model KD, in which the single teacher KD prohibits the student model from distilling information using cross-domain context. To fix this concern, we have looked into learning a lightweight student from a group of teachers. To be more specific, we train five distinct lightweight convolutional neural networks (CNNs) for semantic segmentation on several datasets. Several state-of-the-art augmentation transformations have also been utilized in our training phase. The impacts of such training scenarios are then assessed in terms of student robustness and accuracy. As the main contribution of this paper, our proposed multi-teacher KD paradigm endows the student with the ability to amalgamate and capture a variety of knowledge illustrations from different sources. Results demonstrated that our method outperforms the existing studies on both clean and corrupted data in the semantic segmentation task while benefiting from our proposed score weight system. Experiments validate that our multi-teacher framework results in an improvement of 9% up to 32.18% compared to the singleteacher paradigm. Moreover, it is demonstrated that our paradigm surpasses previous supervised real-time studies in the semantic segmentation challenge.

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Robust Skin Disease Classification by Distilling Deep Neural Network Ensemble for the Mobile Diagnosis of Herpes Zoster

Cited by 31 publications

References 50 publications

Deep Learning Based Detection of Missing Tooth Regions for Dental Implant Planning in Panoramic Radiographic Images

Deep Learning Based Detection of Missing Tooth Regions for Dental Implant Planning in Panoramic Radiographic Images

Skin Cancer Classification With Deep Learning: A Systematic Review

Robust Semantic Segmentation With Multi-Teacher Knowledge Distillation

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