This work presents a technique of working together to accumulate learning between various artificial neural networks, the technique has been successfully adapted to a convolutional network and has shown promising results in the context of traffic light classification.

Optimizing CNN's using Cumulative Learning with Auxiliary Networks for Traffic Light Recognition

Glaucoma is a group of eye diseases characterized by progressive degeneration of the optic nerve, which can lead the patient to blindness, however it can be prevented through diagnosis and treatment of the disease. It is the leading cause of irreversible blindness worldwide and is expected to affect 76 million by 2020. The cup-to-disc ratio technique is a structural examination of the optic nerve used to glaucoma diagnose, and requires an accurate segmentation of the optic disc and cup. Thus, this work proposes an automated method for cup segmentation in fundus images of DRISHTI-GS dataset using a combination of the green channel of RGB image and the provided optic disc mask as input to a modified U-Net convolutional neural network. The results achieved were promising, yielding a mean dice coefficient of 94%, much better than state-of-the-art algorithms. The method could be used for glaucoma diagnosis.

Mask Overlaying: A Deep Learning Approach for Individual Optic Cup Segmentation from Fundus Image

In this presentation we evaluate an approach of transfer learning to automatic semantic segmentation of lungs in a pediatric chest x-ray image based on
knowledge acquired in other datasets. The results are promising and
reach 0.917 of Dice using U-Net in a chest x-ray dataset without lung annotation

Geraldo Braz Júnior

3

Presentations

Optimizing CNN's using Cumulative Learning with Auxiliary Networks for Traffic Light Recognition

Mask Overlaying: A Deep Learning Approach for Individual Optic Cup Segmentation from Fundus Image

Transfer Learning Method Evaluation for Automatic Pediatric Chest X-Ray Image Segmentation

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