Abstract
Similarity in appearance between various skin diseases, often makes it challenging for clinicians to identify the type of skin condition, and the accuracy is highly reliant on the level of expertise. There is also a great degree of subjectivity and inter/intra observer variability found in the clinical practices. In this paper, we propose a method for automatic skin diseases recognition that combines two different types of deep convolutional neural network features. We hold the hypothesis that it is equally important to capture global features such as color and lesion shape, as well as local features such as local patterns within the lesion area. The proposed method leverages deep residual network to represent global information, and bilinear pooling technique which allows to extract local features to differentiate between skin conditions with subtle visual differences in local regions. We have evaluated our proposed method on MoleMap dataset with 32,195 and ISBI-2016 challenge dataset with 1,279 skin images. Without any lesion localisation or segmentation, our proposed method has achieved state-of-the-art results on the large-scale MoleMap datasets with 15 various disease categories and multiple imaging modalities, and compares favorably with the best method on ISBI-2016 Melanoma challenge dataset.
Original language | English |
---|---|
Title of host publication | 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) |
Editors | Simon Warfield, Arrete Munoz-Barrutia |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 986-990 |
Number of pages | 5 |
ISBN (Electronic) | 9781509011728 |
ISBN (Print) | 9781509011735 |
DOIs | |
Publication status | Published - 15 Jun 2017 |
Externally published | Yes |
Event | IEEE International Symposium on Biomedical Imaging (ISBI) 2017 - Melbourne Convention and Exhibition Centre, Melbourne, Australia Duration: 18 Apr 2017 → 21 Apr 2017 Conference number: 14th https://ieeexplore.ieee.org/xpl/conhome/7944115/proceeding (IEEE proceedings) http://biomedicalimaging.org/2017/ |
Conference
Conference | IEEE International Symposium on Biomedical Imaging (ISBI) 2017 |
---|---|
Abbreviated title | ISBI 2017 |
Country/Territory | Australia |
City | Melbourne |
Period | 18/04/17 → 21/04/17 |
Other | ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). The 2017 meeting will include tutorials, and a scientific program composed of plenary talks, invited special sessions, challenges, as well as oral and poster presentations of peer-reviewed papers. |
Internet address |
Keywords
- Bilinear pooling
- Deep convolutional neural network (DCNN)
- Feature fusion
- Skin disease recognition