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Academic literature on the topic 'Retina-net'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Retina-net"

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Provencio, Ignacio, Mark D. Rollag, and Ana Maria Castrucci. "Photoreceptive net in the mammalian retina." Nature 415, no. 6871 (January 2002): 493. http://dx.doi.org/10.1038/415493a.

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Liesegang, Thomas J. "Photoreceptive net in the mammalian retina." American Journal of Ophthalmology 133, no. 5 (May 2002): 739. http://dx.doi.org/10.1016/s0002-9394(02)01447-2.

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Priya, S. Sathiya, and J. G. R. Sathiaseelan. "Enhanced Retina Blood Vessel Segmentation by Super Resolution Generative Adversarial Networks based U-Net." Indian Journal of Science and Technology 14, no. 43 (November 12, 2021): 3246–53. http://dx.doi.org/10.17485/ijst/v14i43.1502.

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Zhao, Shun, Tao Liu, Bowen Liu, and Kun Ruan. "Attention residual convolution neural network based on U-net (AttentionResU-Net) for retina vessel segmentation." IOP Conference Series: Earth and Environmental Science 440 (March 19, 2020): 032138. http://dx.doi.org/10.1088/1755-1315/440/3/032138.

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Tsuboi, S., R. Manabe, and S. Iizuka. "Aspects of electrolyte transport across isolated dog retinal pigment epithelium." American Journal of Physiology-Renal Physiology 250, no. 5 (May 1, 1986): F781—F784. http://dx.doi.org/10.1152/ajprenal.1986.250.5.f781.

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Transport of Na and Cl across the isolated dog retinal pigment epithelium (RPE) choroid was investigated. Under the short-circuit condition, a net Na flux was observed from choroid to retina and a net Cl flux was determined in the opposite direction. The current created by the net flux of these two ions was larger than the short-circuit current (SCC). Addition of 10(-5) M ouabain to the apical side inhibited net fluxes of both Na and Cl, whereas it reduced the SCC 84%. Addition of 10(-4) M furosemide to the apical side inhibited net Cl flux but had no effect on the net Na transport. The 10(-4) M furosemide reduced the SCC 38%. These drugs had no effect when applied to the basal side. Thus the transport of both Na and Cl depends on the Na-K-ATPase in the apical membrane of the dog RPE. A furosemide-sensitive neutral carrier at the apical membrane is suggested for the transport of Cl. Replacement of HCO3 with SO4 in the bathing solution caused an increase in the SCC, indicating the choroid-to-retina movement of HCO3 across the short-circuited dog RPE choroid.
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Momin, Shadab, Yang Lei, Zhen Tian, Justin Roper, Jolinta Lin, Shannon Kahn, Hui-Kuo Shu, Jeffrey Bradley, Tian Liu, and Xiaofeng Yang. "Cascaded mutual enhancing networks for brain tumor subregion segmentation in multiparametric MRI." Physics in Medicine & Biology 67, no. 8 (April 11, 2022): 085015. http://dx.doi.org/10.1088/1361-6560/ac5ed8.

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Abstract Accurate segmentation of glioma and its subregions plays an important role in radiotherapy treatment planning. Due to a very populated multiparameter magnetic resonance imaging image, manual segmentation tasks can be very time-consuming, meticulous, and prone to subjective errors. Here, we propose a novel deep learning framework based on mutual enhancing networks to automatically segment brain tumor subregions. The proposed framework is suitable for the segmentation of brain tumor subregions owing to the contribution of Retina U-Net followed by the implementation of a mutual enhancing strategy between the classification localization map (CLM) module and segmentation module. Retina U-Net is trained to accurately identify view-of-interest and feature maps of the whole tumor (WT), which are then transferred to the CLM module and segmentation module. Subsequently, CLM generated by the CLM module is integrated with the segmentation module to bring forth a mutual enhancing strategy. In this way, our proposed framework first focuses on WT through Retina U-Net, and since WT consists of subregions, a mutual enhancing strategy then further aims to classify and segment subregions embedded within WT. We implemented and evaluated our proposed framework on the BraTS 2020 dataset consisting of 369 cases. We performed a 5-fold cross-validation on 200 datasets and a hold-out test on the remaining 169 cases. To demonstrate the effectiveness of our network design, we compared our method against the networks without Retina U-Net, mutual enhancing strategy, and a recently published Cascaded U-Net architecture. Results of all four methods were compared to the ground truth for segmentation and localization accuracies. Our method yielded significantly (P < 0.01) better values of dice-similarity-coefficient, center-of-mass-distance, and volume difference compared to all three competing methods across all tumor labels (necrosis and non-enhancing, edema, enhancing tumor, WT, tumor core) on both validation and hold-out dataset. Overall quantitative and statistical results of this work demonstrate the ability of our method to both accurately and automatically segment brain tumor subregions.
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Cameron, David A. "Asymmetric retinal growth in the adult teleost green sunfish (Lepomis cyanellus)." Visual Neuroscience 12, no. 1 (January 1995): 95–102. http://dx.doi.org/10.1017/s0952523800007343.

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AbstractPrevious studies on fish retina have suggested that a curved, non-fused embryonic fissure is associated with, and perhaps caused by, asymmetric growth along the retina's marginal germinal zone (where neurons and Miiller glia are added appositionally throughout life). In this report retinal growth was measured directly in adult green sunfish (Lepomis cyanellus), which has a curved, non-fused embryonic fissure. Growth was asymmetric in both small and large fish: ventral and nasal retina grew more than temporal and dorsal retina. This asymmetry was due to different net rates of cellular addition, rather than differential passive expansion. The absolute rates of retinal growth in the centroperipheral direction were roughly exponential functions of fish size—smaller fish grow faster than large fish—but the area of new retina added per unit time did not vary with fish size. Visual implications of asymmetric retinal growth are evaluated.
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Wang, Hua, Jingfei Hu, and Jicong Zhang. "SCRD-Net: A Deep Convolutional Neural Network Model for Glaucoma Detection in Retina Tomography." Complexity 2021 (April 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/9858343.

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Early and accurate diagnosis of glaucoma is critical for avoiding human vision deterioration and preventing blindness. A deep-neural-network model has been developed for the diagnosis of glaucoma based on Heidelberg retina tomography (HRT), called “Seeking Common Features and Reserving Differences Net” (SCRD-Net) to make full use of the HRT data. In this work, the proposed SCRD-Net model achieved an area under the curve (AUC) of 94.0%. For the two HRT image modalities, the model sensitivities were 91.2% and 78.3% at specificities of 0.85 and 0.95, respectively. These results demonstrate a significant improvement over earlier results. In addition, we visualized the network outputs to develop an interpretation of the learned mechanism for discriminating glaucoma and normal images. Thus, the SCRD-Net can be an effective diagnostic indicator of glaucoma during clinical screening. To facilitate SCRD-Net utilization by the scientific community, the code implementation will be made publicly available.
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Osio, A. A., H. Â. Lê, S. Ayugi, F. Onyango, P. Odwe, and S. Lefèvre. "DETECTION OF DEGRADED ACACIA TREE SPECIES USING DEEP NEURAL NETWORKS ON UAV DRONE IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 455–62. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-455-2022.

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Abstract. Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tree trunks on natural environments such as water, mudflats, and natural vegetated areas is challenging due to the mixed colour image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones, with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea trees from six designated plots around Lake Nakuru, Kenya. Motivated by the need to detect fallen trees around the lake, two well-established deep neural networks, i.e. Faster Region-based Convolution Neural Network (Faster R-CNN) and Retina-Net were used for fallen tree detection. A total of 7,590 annotations of three classes on 256×256 image patches were used for this study. Experimental results show the relevance of deep learning in this context, with Retina-Net model achieving 38.9% precision and 57.9% recall.
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Roecklein, K. A., D. L. Wescott, S. F. Smagula, A. M. Soehner, P. L. Franzen, and B. P. Hasler. "0037 Melanopsin Driven Pupil Responses and Physical Activity: Stability of Activity from Day-to-Day in Winter in Seasonal Affective Disorder." Sleep 43, Supplement_1 (April 2020): A15. http://dx.doi.org/10.1093/sleep/zsaa056.036.

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Abstract Introduction The post-illumination pupil response (PIPR) is a measure of the responsivity of intrinsically photosensitive retinal ganglion cells (ipRGCs), and reflects the cell biology of the photoentrainment pathway projecting from the retina to the circadian clock. Adequate signaling from the ipRGCs in the retina to the circadian clock is necessary to result in robust circadian output which we hypothesize would increase inter-daily stability (IS), a non-parametric modeling technique that examines stability of rest activity rhythms across successive days. Methods Participants were aged 18–66 years and recruited from the greater Pittsburgh area during the Winter with Seasonal Affective Disorder who completed both actigraphy and pupillometry (n = 16). PIPR measures were collected after a 1 second red or blue light pulse, and are calculated as the Net difference between red and blue at multiple time frames: at 6 seconds post stimulus (PIPR 6), from 10–30 seconds post-stimulus (PIPR 20), or from 10–40 seconds post-stimulus (PIPR 30). Using actigraphy, inter-daily stability (IS) was calculated as the amount of overall variability in the recording that is accounted for by the typical 24-hour profile, and reflects stability of the mean 24-h profile day-to-day. Results Inter-daily stability (IS) was associated with Net PIPR 20 (Β = 0.561; p = .031) and Net PIPR 30 (Β = 0.551; p = .034; all Β’s are standardized), but not Net PIPR 6 (Β = 0.298; p = .304). Retinal irradiance was calculated for each participant based on age and pupil diameter, to account for age-related differences in transmission of the stimulus to the retina. All raw Net PIPR values were adjusted for calculated retinal irradiance, and gender and time since wake were included as covariates. Conclusion Inter-daily stability (IS) values indicate greater stability of 24-hour activity profiles across days. If reduced responsivity to entraining pulses of light is associated with day-to-day instability in activity rhythms, as shown here, we might expect that amplifying entraining light through environmental changes or bright light therapy would normalize inter-daily stability in SAD, or the reverse, stabilizing activity profiles across days could improve depression and/or normalize retinal ipRGC responsivity. Support NIMH K.A.R. MH103303
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Dissertations / Theses on the topic "Retina-net"

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Mitrengová, Jana. "Klasifikace cév sítnice." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442496.

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The thesis deals with the classification of the retinal blood vessels in retinal image data. The first part of the thesis deals with the anatomy of the human eye and focuses on the description of the retina and its blood circulation. It further describes the principle of fundus camera and experimental video ophthalmoscope. The second part of the thesis is devoted to a literature search of academic publications that deal with the classification of the retinal vessels into arteries and veins. Subsequently, the principle of selected machine learning methods is presented. Based on the literature research, two methods for the classification of the blood vessels were proposed, the first one using the SVM classifier and the second one using the convolutional neural network U-Net. At the end, the analysis of vascular pulsations was performed. The practical part of the thesis was carried out in Matlab programming interface and images from the RITE, IOSTAR and AFIO database were used for classification and the retinal video sequences taken with an experimental video ophthalmoscope were processed in the analysis of pulsations.
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Pescara, Fabrizio. "Augmented Reality Safety Net in Aerodrome Control Tower." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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In my previous bachelor's thesis I studied an algorithm for the improvement of runway efficiency in normal condition: minimizing the occupation of the runway means serving the greatest number of aircraft with the least possible delay. But other important limitations of airports are the ones in force during low visibility when the number of movements allowed undergoes a relevant reduction. A solution could be the use of technology to support air traffic controllers and pilots to re-establish a good level of situational awareness in order to reduce the limitations in force. In Europe, the technological pillar of the ambitious Single European Sky initiative is the Single European Sky ATM Research project (SESAR). Inside the SESAR project, the RETINA concept is regarding the introduction of augmented reality in aerodrome control tower for the improvement of efficiency and capacity of airports. Within the RETINA concept this thesis aims to study the implementation of new safety nets for aerodrome control tower with the support of augmented reality. This study starts from introduction to air traffic services in order to focus on SESAR project and RETINA concept. Then, concept and graphical solution for Conflicting ATC Clearances Safety Net (CATC) are proposed. Due to Covid-19 pandemic in progress during the thesis work, the implementation and validation activities could not be carried out in RETINA simulator of the University of Bologna labs, so different stand-alone implementation in Unity and validation by questionnaire administered to air traffic controllers are used. This thesis work can be considered as the first iteration in the User-Centered Design process for CATC. After the analysis of the feedback, new iteration in which the design is driven and refined by user-centered evaluation could be setup.
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McKeefry, D. J., M. P. Burton, C. Vakrou, B. T. Barrett, and A. B. Morland. "Induced deficits in speed perception by transcranial magnetic stimulation of human cortical areas V5/MT+ and V3A." 2008. http://hdl.handle.net/10454/6093.

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In this report, we evaluate the role of visual areas responsive to motion in the human brain in the perception of stimulus speed. We first identified and localized V1, V3A, and V5/MT+ in individual participants on the basis of blood oxygenation level-dependent responses obtained in retinotopic mapping experiments and responses to moving gratings. Repetitive transcranial magnetic stimulation (rTMS) was then used to disrupt the normal functioning of the previously localized visual areas in each participant. During the rTMS application, participants were required to perform delayed discrimination of the speed of drifting or spatial frequency of static gratings. The application of rTMS to areas V5/MT and V3A induced a subjective slowing of visual stimuli and (often) caused increases in speed discrimination thresholds. Deficits in spatial frequency discrimination were not observed for applications of rTMS to V3A or V5/MT+. The induced deficits in speed perception were also specific to the cortical site of TMS delivery. The application of TMS to regions of the cortex adjacent to V5/MT and V3A, as well as to area V1, produced no deficits in speed perception. These results suggest that, in addition to area V5/MT+, V3A plays an important role in a cortical network that underpins the perception of stimulus speed in the human brain.
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Books on the topic "Retina-net"

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1946-, Vaina Lucia, ed. From the retina to the neocortex: Selected papers of David Marr. Boston: Birkhäuser, 1991.

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Takao, Kumazawa, Kruger Lawrence, and Mizumura Kazue, eds. The polymodal receptor: A gateway to pathological pain. Amsterdam: Elsevier, 1996.

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(Editor), T. Kumazawa, L. Kruger (Editor), and K. Mizumura (Editor), eds. The Polymodal Receptor - A Gateway to Pathological Pain (Progress in Brain Research). Elsevier Science, 1996.

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Book chapters on the topic "Retina-net"

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Chiang, A. M. "CCD Retina and Neural Net Processor." In Robots and Biological Systems: Towards a New Bionics?, 535–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-58069-7_29.

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Tang, Shuyun, Ziming Qi, Jacob Granley, and Michael Beyeler. "U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina." In Ophthalmic Medical Image Analysis, 62–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87000-3_7.

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Pradeep Kumar T., Lilly Florence M., and Fathima G. "YOLO v5 and Faster R-CNN Performance Evaluation of Solid Waste in Object Detection Application." In Advances in Multimedia and Interactive Technologies, 288–304. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6060-3.ch022.

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Object detection is a booming technology that is on par with computer vision and image processing in which an object of a specific type is detected in an image or video. Object detection consists of several approaches like Retina-Net, Single Shot MultiBox Detector (SSD), and Faster R-CNN. These approaches are used in object detection with limited data, but these approaches either run in two algorithms or has high execution time; to overcome these limitations, the authors have used the latest version of Yolo with the custom dataset of solid waste. In this algorithm, an image in the solid waste dataset, which was annotated, labelled, pre-processed, and segmented and a build version is created with the yolo model; this version can either be used directly in the code for online execution or downloaded in the local system for offline execution.
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S., Sudha, Srinivasan A., T. Gayathri Devi, and Mardeni Bin Roslee. "Detection and Classification of Diabetic Retinopathy Using Image Processing Algorithms, Convolutional Neural Network, and Signal Processing Techniques." In Advances in Computational Intelligence and Robotics, 270–80. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8892-5.ch017.

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Diabetic retinopathy (DR) affects blood vessels in the retina and arises due to complications of diabetes. Diabetes is a serious health issue that must be considered and taken care of at the right time. Modern lifestyle, stress at workplaces, and unhealthy food habits affect the health conditions of our body. So the detection of lesions and treatment at an early stage is required. The detection and classification of early signs of diabetic retinopathy can be done by three different approaches. In Approach 1, an image processing algorithm is proposed. In Approach 2, convolutional neural network (CNN-VGG Net 16) is proposed for the classification of fundus images into normal and DR images. In Approach 3, a signal processing method is used for the detection of diabetic retinopathy using electro retinogram signal (ERG). Finally, the performance measures are calculated for all three approaches, and it is found that detection using CNN improves the accuracy.
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Brinton, Daniel A., and Charles P. Wilkinson. "Pneumatic Retinopexy." In Retinal Detachment. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195330823.003.0013.

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Pneumatic retinopexy (PR) is an office-based, sutureless, no-incision alternative to scleral buckling or vitrectomy for the surgical repair of selected retinal detachments. Cryotherapy is applied around the retinal break(s) to form a permanent seal. A gas bubble is injected into the vitreous cavity, and the patient is positioned so that the bubble closes the retinal break(s), allowing resorption of the subretinal fluid (Figure 8–1A–F). As an alternative to cryotherapy, laser photocoagulation can be applied after the intraocular gas has caused the retina to reattach. Sulfur hexafluoride (SF6) is the gas most frequently used with pneumatic retinopexy. Perfluorocarbon gases such as perfluoropropane (C3F8) are sometimes used, and success has also been reported with sterile room air. In selecting a gas, it is important to understand the longevity and expansion characteristics of the gases. SF6 doubles in volume within the eye, reaching its maximum size at about 36 hours. It will generally disappear within about 10–14 days, depending on the amount injected. Perfluoropropane nearly quadruples in volume, reaching maximum size in about three days. The bubble will last 30–45 days in the eye. Room air does not expand, but immediately starts to reabsorb. The air bubble will be gone within just a few days (Table 8–1). The initial expansion of SF6 and C3F8 is due to the law of partial pressures and the solubility coefficients of the gases involved. A 100% SF6 bubble injected into the eye contains no nitrogen or oxygen, but these gases are dissolved in the fluid around the bubble. Due to the law of partial pressures, nitrogen and oxygen will diffuse into the gas bubble. SF6 also starts to diffuse out of the gas bubble into the surrounding fluid which contains no SF6. However, nitrogen and oxygen diffuse across the gas–fluid interface much more quickly than SF6 because of the relative insolubility of SF6. The net result is an initial influx of gas molecules into the bubble, expanding its size until partial pressures equilibrate, net influx equals net egress, and maximum expansion is reached. Then the bubble gradually reabsorbs as the SF6 is slowly dissolved in the surrounding fluid.
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Conference papers on the topic "Retina-net"

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Wu, Yulin, Anqi Liu, Lei Chen, Dong Zhao, Hongchao Zhou, and Qinghe Zheng. "Multi-scale Attention Net for Retina Blood Vessel Segmentation." In CSAI 2020: 2020 4th International Conference on Computer Science and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3445815.3445830.

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Ameri, Nafise, Nasser Shoeibi, and Mojtaba Abrishami. "Segmentation of Hard Exudates in Retina Fundus Images Using BCDU-Net." In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2022. http://dx.doi.org/10.1109/iccke57176.2022.9960101.

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Ahmed Raza, Muhammad, Hajra Bint-e-Naeem, Amanullah Yasin, and Muhammad Haroon Yousaf. "BirdView Retina-Net: Small-Scale Object Detector for Unmanned Aerial Vehicles." In 2021 16th International Conference on Emerging Technologies (ICET). IEEE, 2021. http://dx.doi.org/10.1109/icet54505.2021.9689830.

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Li, Xiao-Min, Geng-Sheng Chen, and Shu-Yang Wang. "Dense-Atrous U-Net with salient computing for Accurate Retina Vessel Segmentation." In 2020 IEEE 15th International Conference on Solid-State & Integrated Circuit Technology (ICSICT). IEEE, 2020. http://dx.doi.org/10.1109/icsict49897.2020.9278165.

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Popat, Vipul, Mahsa Mahdinejad, Oscar Cedeño, Enrique Naredo, and Conor Ryan. "GA-based U-Net Architecture Optimization Applied to Retina Blood Vessel Segmentation." In 12th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010112201920199.

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Fonseca, Pedro Victor de A., Alexandre Carvalho Araújo, João Dallyson S. de Almeida, and Geraldo Braz Júnior. "Treinando Rede Neural Profunda com Divisão Proporcional de Imagens para Segmentação de Estruturas da Retina." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbcas.2022.222421.

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Com o aumento da quantidade de patologias relacionadas ao olho humano, a segmentação da escavação e do disco óptico se tornaram principais objetos de estudo por parte de experimentos interligados a Deep Learning, visando um aperfeiçoamento na classificação das mesmas estruturas possibilitando uma melhor identificação. Este estudo propõe uma abordagem de segmentação da escavação e do disco óptico combinando a técnica de divisão proporcional de imagem, em relação a área de segmentação, e a arquitetura de rede U-Net com encoder da ResNet-34. A abordagem proposta apresentou resultados promissores, alcançando 96% de Dice na segmentação do disco nos datasets RIM-ONE e DRISHTI-GS e 90% e 85% de Dice na segmentação das escavação nos datasets DRISHTI-GS e RIM-ONE, respectivamente.
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