Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images
By ZongYuan Ge, Sergey Demyanov, R. Chakravorty, Adrian Bowling, Rahil Garnavi. 2017
142 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Epidemiology, Oncology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yutong Xie
- Jianpeng Zhang
- Yong Xia
- Chunhua Shen
- Ender Konukoglu
- ZongYuan Ge
- Christian F. Baumgartner
- Jun Zhou
In Epidemiology
Citations in the corpus
- Visual Feature Attribution Using Wasserstein GANs (2017, background): “Pinpointing all evidence of a class is important for a variety of tasks such as weakly supervised localisation or segmentation of structures [41, 43, 65], and better understanding disease effects, and physiological or pathological processes in medical images [67, 18, 12, 19, 13, 27, 54, 30, 31, 63].”
- A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification (2019, background): “Ge et al. [22] reported a multi-modality DCNN that accepts both the clinical and dermoscopic views of a single lesion and is capable of learning single-modality and cross-modality representations, simultaneously.”
- Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images (2018, unknown): “In addition, classifier decision localization has been proposed for multimodal systems [13]; however, localization information alone isn’t sufficient as evidence for classification decisions.”
- Attention Residual Learning for Skin Lesion Classification (2019, methodology): “[29] jointly used the dermoscopy and clinical skin images to train DCNNs and demonstrated the effectiveness of cross-modality learning.”
- Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets (2019, background): “[36], which showed larger accuracy improvements when incorporating clinical and dermoscopic images into a single model, as well as similar diagnosis performance for each modality.”
- Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification (2020, methodology): “Ge et al. [16] considered multi-modality circumstances of skin imaging, which included clinical photography of a macro view and dermoscopy imaging of a micro view of the mole.”