MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641051508 A) filed by Sr University, Warangal, Telangana, on April 22, for 'multi-stream hierarchical attention and feature consistency architecture for real-time interpretable multi-modal brain tumor classification with dynamic calibration and autonomous error mitigation.'
Inventor(s) include Dr. Mohammed Ali Shaik.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "The current invention reveals hierarchical attention and feature consistency architecture with multi-streams in real-time to interpretable classification of multi-modal brain tumors. This system takes in heterogeneous neuroimaging data, including MRI and CT, and runs the processes in parallel deep learning streams, implementing a hierarchical attention mechanism to dynamically focus attention on salient features at multiple scales and modalities. A special feature consistency module makes sure that the data in streams is structurally and semantically consistent, whereas a dynamic calibration engine modulates weights according to the quality of data. The architecture has an autonomous error mitigation unit to track internal consistency and an interpretability layer to give localized heatmaps and feature wise justifications of each diagnosis to increase clinical safety. This combined solution addresses the technical issues of multi-modal misalignment, diagnostic opaque, and system susceptible to noise, and offers a robust and transparent decision support service to neuro-oncology. The invention is very scalable and is optimized to work on low latency in critical medical conditions."
Disclaimer: Curated by HT Syndication.
