scholarly journals Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

2022 ◽  
Vol 71 (2) ◽  
pp. 2477-2495
Author(s):  
Amina Bibi ◽  
Muhamamd Attique Khan ◽  
Muhammad Younus Javed ◽  
Usman Tariq ◽  
Byeong-Gwon Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Lesion Segmentation

Skin lesion segmentation in clinical images using deep learning

2016 ◽  
Author(s):  
M.H. Jafari ◽  
N. Karimi ◽  
E. Nasr-Esfahani ◽  
S. Samavi ◽  
S.M.R. Soroushmehr ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Lesion Segmentation ◽  
Clinical Images

Skin Lesion Segmentation by using Deep Learning Techniques

2019 ◽  
Author(s):  
Sohaib Najat Hasan ◽  
Murat Gezer ◽  
Raghad Abdulaali Azeez ◽  
Sevinc Gulsecen
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Lesion Segmentation ◽  
Learning Techniques

scholarly journals Integrating Domain Knowledge into Deep Learning for Skin Lesion Risk Prioritization to Assist Teledermatology Referral

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 36
Author(s):  
Rafaela Carvalho ◽  
Ana C. Morgado ◽  
Catarina Andrade ◽  
Tudor Nedelcu ◽  
André Carreiro ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Domain Knowledge ◽  
Expert Knowledge ◽  
National Health System ◽  
Additional Patient ◽  
Knowledge Map ◽  
Lesion Segmentation ◽  
Priority Level ◽  
Risk Prioritization

Teledermatology has developed rapidly in recent years and is nowadays an essential tool for early diagnosis. In this work, we aim to improve existing Teledermatology processes for skin lesion diagnosis by developing a deep learning approach for risk prioritization with a dataset of retrospective data from referral requests of the Portuguese National Health System. Given the high complexity of this task, we propose a new prioritization pipeline guided and inspired by domain knowledge. We explored automatic lesion segmentation and tested different learning schemes, namely hierarchical classification and curriculum learning approaches, optionally including additional patient metadata. The final priority level prediction can then be obtained by combining predicted diagnosis and a baseline priority level accounting for explicit expert knowledge. In both the differential diagnosis and prioritization branches, lesion segmentation with 30% tolerance for contextual information was shown to improve classification when compared with a flat baseline model trained on original images; furthermore, the addition of patient information was not beneficial for most experiments. Curriculum learning delivered better results than a flat or hierarchical approach. The combination of diagnosis information and a knowledge map, created in collaboration with dermatologists, together with the priority achieved interesting results (best macro F1 of 43.93% for a validated test set), paving the way for new data-centric and knowledge-driven approaches.

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