Learning to Detect Blue–White Structures in Dermoscopy Images With Weak Supervision

Madooei, Ali; Drew, Mark S.; Hajimirsadeghi, Hossein

doi:10.1109/jbhi.2018.2835405

Cited by 16 publications

(5 citation statements)

References 29 publications

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“…Finally, the segmented ROI was classified using an SVM. Madooei et al [138] utilized a blue-whitish structure to differentiate melanoma from nevi lesions. Saez et al [139] utilized the color of lesions to classify these lesions as melanoma or nevi.…”

Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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“…The experiments performed using the datasets is found to produce results outperforming similar other techniques. Similarly several other research investigations aimed at identifying the improvement on the model with image analysis using local features [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Decision Support System Analysis for Malignant Melanoma Detection

Raju D*,

et al. 2019

IJRTE

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One of the most deadly dangerous disease is cancer which is among human beings. Skin cancer is of different types that is found recently among humans. Melanoma is one such type of skin cancer which causes majority of death rate. Biopsy method leads to conventional clinical diagnosis for detection of melanoma. The study in this paper presents different benchmarking techniques for melanoma prediction and evaluation. The main challenge is detection of malignant melanoma, which is found to have asymmetrical, irregular borders, notched edges and colour variations. The various stages of skin cancer prediction were analyzed in this paper. A detailed study on various techniques of medical image processing as applied to melanoma images for past years which need the more attention which is discussed here. The techniques and methods that exit are helpful in each of these process are evaluated and summarized. The paper aims at presenting an analysis on to identify on investigation efforts required to group and classify the sub categories available in the literature and to provide a summary of all the available methods for identification of melanoma cancer.

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“…Besides CT scans, Madooei et al [37] proposed using a multiple instance learning (MIL) framework for identifying blue-white structure from dermoscopy images based on image-level labels. Chamanzar and Nie [38] proposed a deep learning method to achieve cell segmentation and detection based on point labels.…”

Section: Related Workmentioning

confidence: 99%

Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection

Chang

Lin

Hsieh

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Objective: Common bile duct (CBD) stones caused diseases are life-threatening. Because CBD stones locate in the distal part of the CBD and have relatively small sizes, detecting CBD stones from CT scans is a challenging issue in the medical domain. Methods and procedures: We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed. Results: Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results. Conclusion: We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at after acceptance. Clinical impact: Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases.

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Learning to Detect Blue–White Structures in Dermoscopy Images With Weak Supervision

Cited by 16 publications

References 29 publications

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Decision Support System Analysis for Malignant Melanoma Detection

Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection

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