Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions  , especially in the early stage  , is extremely challenging via dermatologists' observation . Hence, the discovery of reliable biomarkers for melanoma diagnosis will be meaningful  . Recent years,  deep learning empowered computer-assisted diagnosis has been shown its value in medical imaging-based decision making. However, lots of research focus on improving disease detection accuracy but not exploring the evidence of pathology. In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesion . Secondly, we utilize a prediction difference analysis method that examining each patch on the image through patch wised corrupting for detecting the biomarkers. Lastly, we validate that our biomarker findings are corresponding to the patterns in the literature. The findings might be significant to guide clinical diagnosis.
Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions by eye , especially in the early stage of melanoma , is extremely challenging for the dermatologists . Hence, the discovery of reliable biomarkers will be meaningful for melanoma diagnosis . Recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging based decision making. However, much research focuses on improving disease detection accuracy but not exploring the evidence of pathology. In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesions . Secondly, we utilize a prediction difference analysis method that examines each patch on the image through patch-wised corrupting to detect the biomarkers. Lastly, we validate that our biomarker findings are corresponding to the patterns in the literature. The findings can be significant and useful to guide clinical diagnosis.
A
by eye
A
of melanoma
R
via dermatologists' observation
for the dermatologists
D
for melanoma diagnosis
A
for melanoma diagnosis
A
the value of
R
diagnosis
diagnose
R
its value in medical imaging-based
in biomedical imaging based
R
lots of research focus
much research focuses
R
lesion
lesions
R
examining
examines
R
patch wised corrupting for detecting
patch-wised corrupting to detect
R
might be significant
can be significant and useful