"If you can." Neo adopts a martial arts fighting pose, then launches a furious flurry at his mentor, flailing at him with high-speed strikes. Morpheus blocks every attempted attack effortlessly. The scene is, of course, the training sequence from 1999's The Matrix, a movie that blew minds at the time with its combination of artificial intelligence-focused storyline and cutting-edge computer graphics. More than 20 years later, the scene is being used as part of a Facebook demo to show me some of the company's groundbreaking A.I. image recognition technology. On the screen, the scene plays out as normal.

Image registration is a fundamental task in medical image analysis. Recently, deep learning based image registration methods have been extensively investigated due to their excellent performance despite the ultra-fast computational time. However, the existing deep learning methods still have limitation in the preservation of original topology during the deformation with registration vector fields. To address this issues, here we present a cycle-consistent deformable image registration. The cycle consistency enhances image registration performance by providing an implicit regularization to preserve topology during the deformation. The proposed method is so flexible that can be applied for both 2D and 3D registration problems for various applications, and can be easily extended to multi-scale implementation to deal with the memory issues in large volume registration. Experimental results on various datasets from medical and non-medical applications demonstrate that the proposed method provides effective and accurate registration on diverse image pairs within a few seconds. Qualitative and quantitative evaluations on deformation fields also verify the effectiveness of the cycle consistency of the proposed method.

In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance. However, current practices for updating models rely solely on isolated, aggregate performance analyses, overlooking important dependencies, expectations, and needs in real-world deployments. We consider how updates, intended to improve ML models, can introduce new errors that can significantly affect downstream systems and users. For example, updates in models used in cloud-based classification services, such as image recognition, can cause unexpected erroneous behavior in systems that make calls to the services. Prior work has shown the importance of "backward compatibility" for maintaining human trust. We study challenges with backward compatibility across different ML architectures and datasets, focusing on common settings including data shifts with structured noise and ML employed in inferential pipelines. Our results show that (i) compatibility issues arise even without data shift due to optimization stochasticity, (ii) training on large-scale noisy datasets often results in significant decreases in backward compatibility even when model accuracy increases, and (iii) distributions of incompatible points align with noise bias, motivating the need for compatibility aware de-noising and robustness methods.

I am passionate about recruitment and competencies evaluation. I finish my Ph.D. in Industrial & Organizational Psychology in 2017, my dissertation topic was about fairness and discrimination perceptions during a selection process. I was also in Ph.D. Students associations, organizing social and professional events. Now as a recruiter in Science me Up, I really care about being fair and available for all the applicants.

Between 2010-2014, the field of image recognition and analysis was revolutionised by the introduction of deep learning, which enabled unprecedented performance leaps. These rapid advancements are fuelling the development of automated, accurate, accessible, and cost-effective medical diagnostics. Since 2010, over 60 entities including 40 new firms globally have set out to capitalise on these technological advances, seeking to commercialise AI-based diagnostics services in fields such as cancer and cardiovascular disease (CVD). More than $2.2 billion has been invested in new start-ups, with the investment since 2017 being 200% higher than the total since 2010. IDTechEx expects the market for AI-enabled image-based medical diagnostics to grow by nearly 10,000% until 2040 whilst the global addressable market (scan volume regardless of processing method) will grow by 50%.

Massive amount of time series data are generated daily, in areas as diverse as astronomy, industry, sciences, and aerospace, to name just a few. One obvious problem of handling time series databases concerns with its typically massive size--gigabytes or even terabytes are common, with more and more databases reaching the petabyte scale. Most classic data mining algorithms do not perform or scale well on time series data.

We propose Fuzzy Jaccard Index (FUJI) -- a scale-invariant score for assessment of the similarity between two ranked/ordered lists. FUJI improves upon the Jaccard index by incorporating a membership function which takes into account the particular ranks, thus producing both more stable and more accurate similarity estimates. We provide theoretical insights into the properties of the FUJI score as well as propose an efficient algorithm for computing it. We also present empirical evidence of its performance on different synthetic scenarios. Finally, we demonstrate its utility in a typical machine learning setting -- comparing feature ranking lists relevant to a given machine learning task. In real-life, and in particular high-dimensional domains, where only a small percentage of the whole feature space might be relevant, a robust and confident feature ranking leads to interpretable findings as well as efficient computation and good predictive performance. In such cases, FUJI correctly distinguishes between existing feature ranking approaches, while being more robust and efficient than the benchmark similarity scores.

As IBM (IBM) and Red Hat team up with Adobe (ADBE) on artificial intelligence and personalization technology, Adobe stock is trying to customize a new base and buy point. The IBD Long-Term Leader is also setting its sights on a fresh all-time high. In July, Adobe, IBM and Red Hat announced a strategic partnership aimed at accelerating the digital transformation and strengthening of real-time data security for enterprises, with a focus on regulated industries such as banking and health care. Building on IBM's acquisition of Red Hat in 2018, the goal of the partnership is to "enable companies to deliver more personalized experiences across the customer journey, driving improved engagement, profitability and loyalty." Having already made its own successful shift to a software-as-a-service model, Adobe has become a major player in cloud-based creative, personalization and analytics products.

This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

How to implement custom object detection with template matching. Today, state-of-the-art object detection algorithms (algorithms aiming to detect objects in pictures) are using neural networks such as Yolov4. Template matching is a technique in digital image processing for finding small parts of an image that matches a template image. It is a much simpler solution than a neural network to conduct object detection. In my experience, combining a neural network like Yolov4 and object detection with template matching here is a good way to considerably improve your neural network performance! When you use OpenCV template matching, your template slides pixel by pixel on your image.