Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of label noise that can impact the performance. In this study, we propose novel training strategies that handle label noise from such suboptimal data. Prior label probabilities were measured on a subset of training data re-read by 4 board-certified radiologists and were used during training to increase the robustness of the training model to the label noise. Furthermore, we exploit the high comorbidity of abnormalities observed in chest radiography and incorporate this information to further reduce the impact of label noise. Additionally, anatomical knowledge is incorporated by training the system to predict lung and heart segmentation, as well as spatial knowledge labels. To deal with multiple datasets and images derived from various scanners that apply different post-processing techniques, we introduce a novel image normalization strategy. Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets. With an average AUC score of 0.880 across all abnormalities, our proposed training strategies can be used to significantly improve performance scores.

This paper presents a novel supervised approach to detecting the chorus segments in popular music. Traditional approaches to this task are mostly unsupervised, with pipelines designed to target some quality that is assumed to define "chorusness," which usually means seeking the loudest or most frequently repeated sections. We propose to use a convolutional neural network with a multi-task learning objective, which simultaneously fits two temporal activation curves: one indicating "chorusness" as a function of time, and the other the location of the boundaries. We also propose a post-processing method that jointly takes into account the chorus and boundary predictions to produce binary output. In experiments using three datasets, we compare our system to a set of public implementations of other segmentation and chorus-detection algorithms, and find our approach performs significantly better.

Artificial intelligence is one of the most revolutionary technologies of our time, which is advancing as each day goes by. AI labs contribute to these advancements by housing scientists and researchers under one roof to study this disruptive technology for further developments. While there are quite a few AI labs across the globe, artificial intelligence researchers go perplexed when people ask them to rate the top labs in the world. And rightfully so, because they're all unique in the way they work. While every lab focuses on different domains of artificial intelligence, commercial AI labs like Google, Facebook, Amazon, Apple, and Microsoft, the U.S Big Tech, have set up dedicated AI labs too.

The Indian Institute of Technology Madras has developed a fellowship program to encourage early-career AI researchers. The Narayanan Family Foundation and the Institute's Robert Bosch Centre for Data Science and AI have teamed up to build a fellowship in Artificial Intelligence for Social Good. The application is available to artificial intelligence researchers who want to use their skills for the betterment. The Indian Institute of Technology Madras hopes to attract recent PhD graduates or newly qualified researchers in computer science, computational and data sciences, biomedical sciences, management, finance, and other engineering departments with outstanding educational achievements to RBCDSAI through this program, which is funded by the Narayanan Family Foundation. With India's largest network analytics and deep reinforcement learning study groups, RBCDSAI is a world's most prominent interdisciplinary research academic centre for Data Science and AI.

As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning …

This is the concluding part of our blog where we focus on extracting actionable items from stand-up meetings. You can find the previous blog post here. Just to summarize we looked at how GPT-3 summarized standup calls without any text pre-processing. The summary was good but there was nothing actionable that we could extract because " Who did what " details are important to make something actionable. So in order to improve results, lets think of a standup meeting flow in terms of a block diagram.

In this tutorial, you will learn how to perform face detection with OpenCV and Haar cascades. I've been an avid reader for PyImageSearch for the last three years, thanks for all the blog posts! My company does a lot of face application work, including face detection, recognition, etc. We just started a new project using embedded hardware. I don't have the luxury of using OpenCV's deep learning face detector which you covered before, it's just too slow on my devices.

For a very long time, humans have been trying to design a machine that has complex capabilities like how human brain does. When artificial intelligence first came into existence, people thought that making a model that imitates humans will be easy. But it took more than five decades for scientists to turn the concept successful. Today, we are running after machines that carry the cognitive capabilities of human brain in it. Why is designing a mechanism that is similar to human brain complex?

Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit. Machine learning (ML), artificial intelligence (AI), and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. The potential uses include improving diagnostic accuracy,1 more reliably predicting prognosis,2 targeting treatments,3 and increasing the operational efficiency of health systems.4 Examples of potentially disruptive technology with early promise include image based diagnostic applications of ML/AI, which have shown the most early clinical promise (eg, deep learning based algorithms improving accuracy in diagnosing retinal pathology compared with that of specialist physicians5), or natural language processing used as a tool to extract information from structured and unstructured (that is, free) text embedded in electronic health records.2 Although we are only just …

As more and more robots are envisioned to cooperate with humans sharing the same space, it is desired for robots to be able to predict others' trajectories to navigate in a safe and self-explanatory way. We propose a Convolutional Neural Network-based approach to learn, detect, and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly, the proposed approach unveils the obscurity in the previous use of a pooling layer, presenting a way to intuitively explain the decision-making process.