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Classifying different Retinal degeneration from Optical Coherence Tomography Images (OCT).

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MobileNetV2: Inverted Residuals and Linear Bottlenecks

CVPR 2018 tensorflow/models

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.

IMAGE CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION OBJECT DETECTION PEDESTRIAN ATTRIBUTE RECOGNITION PEDESTRIAN TRAJECTORY PREDICTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

Rethinking the Inception Architecture for Computer Vision

CVPR 2016 tensorflow/models

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.

IMAGE CLASSIFICATION RETINAL OCT DISEASE CLASSIFICATION

Xception: Deep Learning With Depthwise Separable Convolutions

CVPR 2017 keras-team/keras-applications

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).

IMAGE CLASSIFICATION RETINAL OCT DISEASE CLASSIFICATION

Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

13 Oct 2019SharifAmit/OpticNet-71

Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task.

RETINAL OCT DISEASE CLASSIFICATION

Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

23 Oct 2020Valentyn1997/oct-diagn-semi-supervised

Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.

RETINAL OCT DISEASE CLASSIFICATION SEMI-SUPERVISED IMAGE CLASSIFICATION TRANSFER LEARNING

Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images

16 May 2020SharifAmit/Robust_Joint_Attention

Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases.

RETINAL OCT DISEASE CLASSIFICATION