Multi-Class Lesion Diagnosis with Pixel-wise Classification Network
This work addresses skin lesion diagnosis for medical imaging applications, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of multi-class lesion diagnosis in dermoscopic images by using a pixel-wise classification network (DeeplabV3+) with post-processing methods, achieving results submitted to the ISIC Challenge 2018 without reporting specific performance numbers.
Lesion diagnosis of skin lesions is a very challenging task due to high inter-class similarities and intra-class variations in terms of color, size, site and appearance among different skin lesions. With the emergence of computer vision especially deep learning algorithms, lesion diagnosis is made possible using these algorithms trained on dermoscopic images. Usually, deep classification networks are used for the lesion diagnosis to determine different types of skin lesions. In this work, we used pixel-wise classification network to provide lesion diagnosis rather than classification network. We propose to use DeeplabV3+ for multi-class lesion diagnosis in dermoscopic images of Task 3 of ISIC Challenge 2018. We used various post-processing methods with DeeplabV3+ to determine the lesion diagnosis in this challenge and submitted the test results.