"Avengers Assemble" 1:5 scale Iron Man 15.75 inches tall statue by Sideshow Collectibles

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Iron Man was always qualified to become an Avenger. It was just in his makeup. It was who he was. Tony Stark, on the other hand, it’s never been as cut and dry. Which is why this is an Iron Man Statue and not one of Mr. Stark.

Depicting the now instantly recognizable armor and standing at 15.75” tall, this 1:5 scale statue features light up hands and chest and is crafted out of resin. The Sideshow Exclusive variation includes the classic Iron Man portrait as a swap-out option.

Don’t miss out on Iron Man from our ‘Avengers Assemble’ Statue collection, which will also include Captain America, Thor, and Hulk in the future!

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Sideshow Exclusive: classic Iron Man portrait as a swap-out option

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Deep Learning for Detection of Diabetic Eye Disease

Posted by Lily Peng MD PhD, Product Manager and Varun Gulshan PhD, Research Engineer

Diabetic retinopathy (DR) is the fastest growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide. If caught early, the disease can be treated; if not, it can lead to irreversible blindness. Unfortunately, medical specialists capable of detecting the disease are not available in many parts of the world where diabetes is prevalent. We believe that Machine Learning can help doctors identify patients in need, particularly among underserved populations.

A few years ago, several of us began wondering if there was a way Google technologies could improve the DR screening process, specifically by taking advantage of recent advances in Machine Learning and Computer Vision. In “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs“, published today in JAMA, we present a deep learning algorithm capable of interpreting signs of DR in retinal photographs, potentially helping doctors screen more patients in settings with limited resources.

One of the most common ways to detect diabetic eye disease is to have a specialist examine pictures of the back of the eye (Figure 1) and rate them for disease presence and severity. Severity is determined by the type of lesions present (e.g. microaneurysms, hemorrhages, hard exudates, etc), which are indicative of bleeding and fluid leakage in the eye. Interpreting these photographs requires specialized training, and in many regions of the world there aren’t enough qualified graders to screen everyone who is at risk.

Figure 1. Examples of retinal fundus photographs that are taken to screen for DR. The image on the left is of a healthy retina (A), whereas the image on the right is a retina with referable diabetic retinopathy (B) due a number of hemorrhages (red spots) present.

Working closely with doctors both in India and the US, we created a development dataset of 128,000 images which were each evaluated by 3-7 ophthalmologists from a panel of 54 ophthalmologists. This dataset was used to train a deep neural network to detect referable diabetic retinopathy. We then tested the algorithm’s performance on two separate clinical validation sets totalling ~12,000 images, with the majority decision of a panel 7 or 8 U.S. board-certified ophthalmologists serving as the reference standard. The ophthalmologists selected for the validation sets were the ones that showed high consistency from the original group of 54 doctors.

Performance of both the algorithm and the ophthalmologists on a 9,963-image validation set are shown in Figure 2.

Figure 2. Performance of the algorithm (black curve) and eight ophthalmologists (colored dots) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on a validation set consisting of 9963 images. The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high sensitivity and high specificity operating points.

The results show that our algorithm’s performance is on-par with that of ophthalmologists. For example, on the validation set described in Figure 2, the algorithm has a F-score (combined sensitivity and specificity metric, with max=1) of 0.95, which is slightly better than the median F-score of the 8 ophthalmologists we consulted (measured at 0.91).

These are exciting results, but there is still a lot of work to do. First, while the conventional quality measures we used to assess our algorithm are encouraging, we are working with retinal specialists to define even more robust reference standards that can be used to quantify performance. Furthermore, interpretation of a 2D fundus photograph, which we demonstrate in this paper, is only one part in a multi-step process that leads to a diagnosis for diabetic eye disease. In some cases, doctors use a 3D imaging technology, Optical Coherence Tomography (OCT), to examine various layers of a retina in detail. Applying machine learning to this 3D imaging modality is already underway, led by our colleagues at DeepMind. In the future, these two complementary methods might be used together to assist doctors in the diagnosis of a wide spectrum of eye diseases.

Automated DR screening methods with high accuracy have the strong potential to assist doctors in evaluating more patients and quickly routing those who need help to a specialist. We are working with doctors and researchers to study the entire process of screening in settings around the world, in the hopes that we can integrate our methods into clinical workflow in a manner that is maximally beneficial. Finally, we are working with the FDA and other regulatory agencies to further evaluate these technologies in clinical studies.

Given the many recent advances in deep learning, we hope our study will be just one of many compelling examples to come demonstrating the ability of machine learning to help solve important problems in medical imaging in healthcare more broadly.

Learn more about the Health Research efforts of the Brain team at Google

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Hot Toys Rogue One: A Star Wars Story 1/6th scale Chirrut Îmwe 12-inch Collectible Figure

“I fear nothing. All is as the Force wills it.”

Fans around the world are all anticipating the new adventure in a galaxy far, far away… as Rogue One: A Star Wars Story is getting closer to release! One of the most exciting new characters introduced in this epic film is Chirrut Îmwe, a blind warrior monk from Jedha. Chirrut believes all living things are connected through the Force, and his profound spirituality makes him a formidable warrior crucial to the Rebels’ desperate attempt to steal the plans of the Empire’s devastating super-weapon – the Death Star.

After announcing a string of Imperial characters from the movie, Hot Toys is pleased to put the Rebels in the spotlight. Today we are thrilled to present the 1/6th scale collectible figure of Chirrut Îmwe! Specially crafted based on the character’s appearance in the movie, the extremely life-like collectible figure features a newly developed head sculpt, a meticulously tailored costume, detailed weapons and accessories including a staff and a crossbow, and a specially designed figure stand.

Furthermore, in celebration of the famed Hong Kong actor and martial artist Donnie Yen-甄子丹 Official starring as the spiritual warrior Chirrut, Hot Toys is excited to present a special Deluxe version of the 1/6th scale Chirrut Îmwe only available in selected markets. This Deluxe Version will exclusively feature a diorama figure base with a Stormtrooper helmet and a special package design inspired by traditional Asian art!

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Hot Toys MMS402 – 403 Rogue One: A Star Wars Story 1/6th scale Chirrut Îmwe Collectible Figure features: Authentic and detailed likeness of Donnie Yen as Chirrut Îmwe in Rogue One: A Star Wars Story | Movie-accurate facial expression with detailed skin texture | Approximately 29 cm tall Body with over 30 points of articulations | Nine (9) pieces of interchangeable hands including: One (1) pair of fists, One (1) pair of open hands, One (1) pair of hands for holding staff, One (1) pair of hands for holding crossbow, One (1) left hand for making fighting gestures

Costume: One (1) dark blue robe with folded cape on the back, One (1) red tunic, One (1) white bandolier, One (1) gauntlet on the left wrist, One (1) pair of black pants, One (1) pair of black boots

Weapons: One (1) staff with mechanical parts, One (1) articulated crossbow that can be slung over the shoulder

Regular Version (MMS402) Accessory: Specially designed rectangular figure stand with Star Wars logo

Deluxe Version (MMS403) Exclusive Features: One (1) specially designed diorama figure base, One (1) Stormtrooper helmet (not wearable on figure), Specially crafted package design inspired by traditional Asian art

The base looks like the one that was came with the Hot Toys MMS90 Movie Masterpiece Series 1/6th scale 14-inch Predator collectible figure – see the action figure review posted HERE

Release date: Q1 – Q2, 2017

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