Titlebook: Ophthalmic Medical Image Analysis; 7th International Wo Huazhu Fu,Mona K. Garvin,Yalin Zheng Conference proceedings 2020 Springer Nature Sw

上下連貫

What is the Optimal Attribution Method for Explainable Ophthalmic Disease Classification?,r was done by a panel of eye care clinicians who rated the methods based on their correlation with diagnostic features. The study emphasizes the need for developing explainable models that address the end-user requirements, hence increasing the clinical acceptance of deep learning.

freight

Cascaded Attention Guided Network for Retinal Vessel Segmentation,. Both of the two modules adopt an Attention UNet++ (AU-Net++) to boost the performance, which employs Attention guided Convolutional blocks (AC blocks) on the decoder. The experimental results show that our proposed network achieved state-of-the-art performance on the three public retinal datasets DRIVE, CHASE_DB1 and STARE.

使困惑

Automated Detection of Diabetic Retinopathy from Smartphone Fundus Videos,ng the lens with a circle Hough Transform, detecting informative frames using a Support Vector Machine, and detecting the disease itself with an attention-based multiple instance learning (MIL) CNN architecture. Our results support the feasibility of a smartphone video based approach.

嫌惡

978-3-030-63418-6Springer Nature Switzerland AG 2020

menopause

Ophthalmic Medical Image Analysis978-3-030-63419-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

膽小懦夫

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/o/image/702385.jpg

accomplishment

https://doi.org/10.1007/978-3-030-63419-3artificial intelligence; color image processing; color images; computer vision; computer-aided detection

真繁榮

DR Detection Using Optical Coherence Tomography Angiography (OCTA): A Transfer Learning Approach wideep learning algorithms in the medical domain. However, they generally require large amount of manually graded images which may not always be available. In our study, we aim to investigate whether transfer learning can help in identifying patient status from a relatively small dataset.Additionally,

暫時(shí)休息

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Encoder-Decoder Networks for Retinal Vessel Segmentation Using Large Multi-scale Patches,t multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M. Furthermore, we show that thi