You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this work, we propose an advanced AI based grading system for OCT images. The proposed system is a very deep fully convolutional attentive classification network trained with end to end advanced transfer learning with online random augmentation. It uses quasi random augmentation that outputs confidence values for diseases prevalence during inference. Its a fully automated retinal OCT analysis AI system capable of pathological lesions understanding without any offline preprocessing postprocessing step or manual feature extraction. We present a state of the art performance on the publicly available Mendeley OCT dataset.

Cited Abstract(s):
[1]: In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location information of the optic disc and the macula region for severity prediction. It can efficiently handle luminance variation and it is suitable for varied sized exudates. The method has been probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate detection the proposed method achieved a sensitivity of 96.54 and prediction of 98.35 in DIARETDB1V2 database.

[2]: In this work, we propose advanced pneumonia and Tuberculosis grading system for X-ray images. The proposed system is a very deep fully convolutional classification network with online augmentation that outputs confidence values for diseases prevalence. Its a fully automated system capable of disease feature understanding without any offline preprocessing step or manual feature extraction. We have achieved state- of-the- art performance on the public databases such as ChestXray-14, Mendeley, Shenzhen Hospital X-ray and Belarus X-ray set.

[3]: 

[4]: In this work, we propose a novel microaneurysm (MA) detection for early diabetic retinopathy screening using color fundus images. Since MA usually the first lesions to appear as an indicator of diabetic retinopathy, accurate detection of MA is necessary for treatment. Each pixel of the image is classified as either MA or non-MA using a deep neural network with dropout training procedure using maxout activation function. No preprocessing step or manual feature extraction is required. Substantial improvements over standard MA detection method based on the pipeline of preprocessing, feature extraction, classification followed by post processing is achieved. The presented method is evaluated in publicly available Retinopathy Online Challenge (ROC) and Diaretdb1v2 database and achieved state-of-the-art accuracy.
Deep learning is successfully applied in many applications of the medical image analysis domain. Tasks related to features segmentation [1] and feature classification [3] can be performed using both traditional computer vision and deep learning. Deep learning recently captured momentum to outperform traditional features based methods used in the medical image domain. Starting from retinal analysis to cancer analysis, deep learning proved its usefulness as screening and diagnostic tools for doctors. In the eye [4] and X-ray [2] screening scenarios deep learning outperforms human practitioners.