SenseTime recently launched its new app, 'Magic Photo Capture', providing users with a creative and fun AI imaging experience. With the arrival of the Lunar New Year, this app aims to help users create personalized New Year photos using advanced artificial intelligence technology, enhancing the festive atmosphere. 'Magic Photo Capture' is not just an ordinary photography app; it includes a variety of templates in its mini-program, allowing users to choose from graduation photos, creative group photos, classic art style portraits, travel photos, new national style, and more.

Recently, SenseTime announced the official launch of its brand new app 'Instant Photo Fun', bringing users a creative and fun AI imaging experience. This app is launched in time for the Lunar New Year, aiming to use AI technology to create personalized New Year portraits for users and foster a rich festival atmosphere.

MRI images have long been a significant challenge in medical imaging analysis due to their complexity and large data volume. To train large language models (LLMs) for MRI analysis, developers have had to slice the captured images into 2D images. While this approach is feasible, it limits the model's ability to analyze complex anatomical structures, particularly in intricate cases such as brain tumors, skeletal diseases, or cardiovascular conditions. Image source note: Image generated by AI, licensed by Midjourney. However, GE Healthcare this year...

Xiaomi today revealed the imaging configuration details of the REDMI K80 series smartphones, marking a comprehensive upgrade in photography capabilities for this series. The REDMI K80 series will feature a flagship high dynamic main camera (Light and Shadow Hunter 800 sensor, supporting OIS optical stabilization), a flagship floating telephoto lens (50MP) and a high pixel ultra-wide angle lens (32MP), to meet users' demands for high-quality photography.

American scientists recently published significant research findings in the journal Nature: the artificial intelligence model FastGlioma, jointly developed by the University of Michigan and the University of California, San Francisco, can quickly assess the presence of cancerous tumor residue within ten seconds during brain tumor surgeries, bringing a revolutionary breakthrough to neurosurgery. This innovation combines microscopic optical imaging with AI foundational models. The research team utilized over 11,000 surgical samples and 4 million microscopic images for pre-training, employing the University of Michigan's self-developed stimulated Raman tissue imaging technology.

In recent years, non-destructive imaging technology has made rapid advancements in the field of painting research and preservation. Among these, macro X-ray fluorescence (MA-XRF) analysis stands out, helping experts identify pigments and analyze painting techniques, providing valuable insights into the artist's creative process. However, MA-XRF technology generates large and complex datasets that challenge traditional data analysis methods. Recently, Italian researchers applied deep learning algorithms to spectral analysis of MA-XRF datasets, developing a brand new analysis approach.

Meitu Inc.'s financial report for the first half of 2024 shows a total revenue of 1.62 billion yuan, a year-over-year increase of 28.6%, with an adjusted net profit of 270 million yuan, representing an 80.3% growth. The increase in revenue and net profit is mainly attributed to the advancement of generative AI technology, particularly in the application of imaging and design products. Subscription revenue reached 930 million yuan, reflecting a year-over-year growth of 54.5%, accounting for 57.4% of total revenue. As of June 30, the number of paid subscribers exceeded 10.81 million, with a penetration rate of approximately 4.2%. Meitu's Design Studio focuses on e-commerce design.

"MedTrinity-25M" is a large-scale multimodal medical dataset released by the UCSC-VLAA team, containing 25 million medical images along with detailed annotations. This dataset features multi-granularity annotations, supports the training of multimodal large medical models, and involves a complex construction process that includes data processing, metadata extraction, region of interest localization, and the collection of medical knowledge. Detailed descriptions are generated using large-scale language models to enhance data usability. The dataset was officially released on July 21, 2024, accompanied by a pre-trained model.

MedSAM-2, based on the Segment Anything Model 2 (SAM2) framework, brings revolutionary changes to the field of medical image segmentation. Its core breakthrough treats 2D and 3D images as video sequences, enabling efficient segmentation of three-dimensional images. The 'one-click segmentation' feature allows users to specify a target in a single image, and the model can automatically recognize and accurately segment the same type of object in subsequent images without the need for continuous input. As a general model, it can handle any image.

Perceptive has developed the world's first fully automated AI robot dentist, successfully performing the first complete dental surgery on human patients at a speed 8 times faster than that of human doctors. This innovation introduces the OCT3D imaging system, which uses optical coherence tomography technology to generate 3D images of the inside of teeth, providing high resolution and precise location accuracy, superior to traditional X-rays, and without radiation hazards. The motion structure method adopted by Perceptive addresses the issue of image blurriness, achieving pathological localization accuracy of 20 microns.

The Xiaomi 14 Ultra has been released, featuring AI large model imaging technology, starting at 6499 Yuan. It targets the high-end market, utilizing Xiaomi's Dragon Armor architecture and Jinsha River battery technology to support satellite communication systems and various innovative functions.

🐶 Studies have proven the accuracy of AI and thermal imaging technology in screening for subcutaneous and dermal masses in dogs. 🔬 Research by HT Vista conducted two phases of validation using machine learning algorithms. 🏥 This innovative system is expected to serve as an auxiliary diagnostic tool, aiding in early cancer detection.

By 2035, the number of medical imaging AI tools approved by the FDA is expected to skyrocket, with venture capital support exceeding $30 billion. Over the past seven years, the availability of medical imaging AI products has continued to grow, with medical imaging accounting for 85% of digital health venture capital. Research indicates a strong correlation between venture capital support and the development of AI products, with an estimated 350 new products expected to enter the market by 2035.

The global AI medical imaging market is projected to reach $29.8 billion by 2032. North America holds the largest market share, but the Asia-Pacific region is expected to grow the fastest. Applications of artificial intelligence in radiology and the demand for CT scans are driving market growth. Deep learning and computer vision are the primary technologies, with breast imaging and orthopedic applications being dominant.

OBIA includes 937 patients and 1.93 million medical images from the 301 Hospital, covering 9 modalities and 30 anatomical sites. OBIA realizes the de-identification, management, and quality control of imaging data, providing users with browsing, retrieval, and download services. OBIA fills the gap in the open sharing of biomedical imaging data in the country and helps promote the development of medical imaging AI.