RESUMO
We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.
RESUMO
Colorectal cancer is one of the highest causes of cancer-related death and the patient's survival rate depends on the stage at which polyps are detected. Polyp segmentation is a challenging research task due to variations in the size and shape of polyps leading to necessitate robust approaches for diagnosis. This paper studies the deep generative convolutional framework for the task of polyp segmentation. Here, the analysis of polyp segmentation has been explored with the pix2pix conditional generative adversarial network. On CVC- Clinic dataset, the proposed network achieves Jaccard index of 81.27% and Dice index of 88.48%.