Automatic Severity Rating for Improved Psoriasis Treatment

[1]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[2]  Yong Wang,et al.  PSENet: Psoriasis Severity Evaluation Network , 2020, AAAI.

[3]  Anabik Pal,et al.  Severity Assessment of Psoriatic Plaques Using Deep CNN Based Ordinal Classification , 2018, OR 2.0/CARE/CLIP/ISIC@MICCAI.

[4]  A. Ormerod,et al.  Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma , 2009, The British journal of dermatology.

[5]  Rainer Hofmann-Wellenhof,et al.  A deep learning system for differential diagnosis of skin diseases , 2019, Nature Medicine.

[6]  G Rassner,et al.  Clinical and Laboratory Investigations Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions , 2004 .

[7]  Gerard de Haan,et al.  Automatic imaging sysem with decision support for inspection of pigmented skin lesions and melanoma diagnosis. , 2009 .

[8]  Xuejun Zhang,et al.  Dermatology in China. , 2015, The journal of investigative dermatology. Symposium proceedings.

[9]  Jacob Andreas,et al.  Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment , 2020, MICCAI.

[10]  Tobias Fuchs,et al.  Design of an Algorithm for Automated, Computer-Guided PASI Measurements by Digital Image Analysis , 2018, Journal of Medical Systems.

[11]  M. Herdman,et al.  A study examining inter‐ and intrarater reliability of three scales for measuring severity of psoriasis: Psoriasis Area and Severity Index, Physician's Global Assessment and Lattice System Physician's Global Assessment , 2006, The British journal of dermatology.