Stroke is among the top three causes of death worldwide, and accurate identification of stroke lesion boundaries is critical for diagnosis and treatment. Supervised deep learning methods have emerged as the leading solution for stroke lesion segmentation but require large, diverse, and annotated datasets. The ISLES'24 challenge addresses this need by providing longitudinal stroke imaging data, including CT scans taken on arrival to the hospital and follow-up MRI taken 2-9 days from initial arrival, with annotations derived from follow-up MRI. Importantly, models submitted to the ISLES'24 challenge are evaluated using only CT inputs, requiring prediction of lesion progression that may not be visible in CT scans for segmentation. Our winning solution shows that a carefully designed preprocessing pipeline including deep-learning-based skull stripping and custom intensity windowing is beneficial for accurate segmentation. Combined with a standard large residual nnU-Net architecture for segmentation, this approach achieves a mean test Dice of 28.5 with a standard deviation of 21.27.

As the adoption of Artificial Intelligence (AI) systems within the clinical environment grows, limitations in bandwidth and compute can create communication bottlenecks when streaming imaging data, leading to delays in patient care and increased cost. As such, healthcare providers and AI vendors will require greater computational infrastructure, therefore dramatically increasing costs. To that end, we developed ISLE, an intelligent streaming framework for high-throughput, compute- and bandwidth- optimized, and cost effective AI inference for clinical decision making at scale. In our experiments, ISLE on average reduced data transmission by 98.02% and decoding time by 98.09%, while increasing throughput by 2,730%. We show that ISLE results in faster turnaround times, and reduced overall cost of data, transmission, and compute, without negatively impacting clinical decision making using AI systems.

Lu, who wears a black T-shirt and glasses, pulls a bag out from under his scooter seat then heads down to the beach. "Oh!" exclaims the 43-year-old once making it down to the shore. Lu bends down to pick up a worn plastic bottle that has washed up beside his foot and puts it in his bag. "Wet Chinese plastic," he says flatly. He points to the simplified Chinese characters on the packaging indicating the bottle's origin – mainland China. Lu is not at the beach to stroll among the rusty anti-landing spikes protruding from concrete blocks – a reminder of Kinmen's role as a front-line island between China and Taiwan. Nor has he come to marvel at the lights that now glitter in the dusk from the skyscrapers of the Chinese metropolis of Xiamen, less than 10km (6.2 miles) away across Xiamen Bay. Instead, he has come to the western coast of Kinmen to collect rubbish. When Lu is not working as an administrator at a local tourism office, he contributes to keeping Kinmen clean by picking up rubbish. Tides, weather and ocean currents as well as Kinmen's proximity to Xiamen and the mouth of the polluted Jiulong River in China have left the island exposed to large quantities of waste.

To translate the Standard Bank Group (SBG) data vision and strategy into applicable data strategies in Standard Bank Offshore to support SBG objectives. To oversee the implementation of the data strategy by co-ordinating and facilitating data programmes to enable data driven business decisions that are consistent and effective. To enforce governance and compliance ensuring alignment to SBG framework, policies, and standards. To Provide the business leadership role that has the primary enterprise accountability for value creation by means of the organization's data and analytics assets, as well as the data and analytics ecosystem. To define, develop, and execute the data monetisation strategy by providing guidance, input and leadership across the data, analytics using AI, ML, and advanced data science methodologies.

UK pharmacy Boots has completed a test flight from Portsmouth to the Isle of Wight which involved prescription-only medicines being delivered by drone. The flight departed from the British Army's Baker Barracks on Thorney Island near Portsmouth and arrived at St. Mary's Hospital on the Isle of Wight. Boots collected the medicines and transported them to its pharmacies across the island, where they will be distributed to patients with prescriptions for them. Rich Corbridge, chief information officer at Boots, said: "Drones have huge potential in the delivery of medicines, and it is incredibly exciting to be the first community pharmacy in the UK to transport them in this way. "An island location like the Isle of Wight seemed like a sensible place to start a trial of drones and their value to the delivery of medicines to more remote locations is very clear.

The UK's National Health Service has announced that it will test delivering vital chemotherapy drugs via drone to the Isle of Wight. The body has partnered with Apian, a drone technology startup founded by former NHS doctors and former Google employees. Test flights are due to begin shortly, and it's hoped that the system will reduce journey times for the drugs, cut costs and enable cancer patients to receive treatment far more locally. The Isle of Wight is an island two miles off the south coast of England with a population just under 150,000. Due to the short shelf-life of most chemotherapy drugs, medicines are either rushed onto the island or patients take the ferry to the mainland.

The NHS plans to use drones to fly chemotherapy drugs to cancer patients in England to avoid the need for long journeys to collect them. The devices will transport doses from Portsmouth to the Isle of Wight in a trial that, if successful, will lead to drones being used for similar drops elsewhere. They will take 30 minutes to travel across the Solent, which will save patients on the island a three to four-hour round trip by ferry or hovercraft. On Tuesday, Amanda Pritchard, NHS England's chief executive, unveiled the move to help mark the 74th anniversary of the health service's creation by the postwar Labour government. "Delivering chemo by drone is another extraordinary development for cancer patients and shows how the NHS will stop at nothing to ensure people get the treatment they need as promptly as possible, while also cutting costs and carbon emissions," she said.

With the increase of data volume and the continuous development in deep learning, although more and more traditional machine learning techniques are outperformed by artificial neural networks, tree-based methods are still popular. Random forest (Breiman, 2001) is commonly used as a benchmark to evaluate the performance of nonparametric models, while XGBoost (Chen and Guestrin, 2016) performs well in Kaggle competitions and often competes with artificial neural networks. Also, instead of relying on a specific method, people prefer to make decisions based on a combination of multiple models, which shows a better performance than a single one. Therefore, identifying the importance of each model by weights assignment is critical.

For years, the answer was by hard graft, with rangers checking burrows and nests for birds and eggs, and observers forced to sit for hours at a time armed with clipboards and no little patience. But in a marriage of nature and cutting edge technology, the arduous task of establishing the puffin population on the Isle of May is being carried out using artificial intelligence, machine learning, and image recognition software. Those behind the project believe it could help minimise disruption to birds' breeding and feeding habits, particularly when faced with developments such as offshore windfarms. The initiative uses four cameras placed in stainless steel boxes at various points of the island in the Firth of Forth in order to capture live footage of the puffins. Each box has a condensation heater as well as a backup power supply.

It's not just online and big-box retailers that are exploring deliveries by drone. Following in the footsteps of the Swiss Post, the UK's Royal Mail is the latest postal service to trial drone flights. The company has announced a landmark project to deliver packages -- including personal protective equipment, COVID testing kits and assorted mail -- to a UK island using an autonomous Uncrewed Aerial Vehicle (UAV). As part of the government-backed project, a large drone will take off from the mainland and fly to the Scilly Isles (a remote archipelago off the Cornish coast in southwest England). The twin-engine UAV can carry up to 100kg of mail of all shapes and sizes, which the Royal Mail said is equivalent to a typical delivery round.