Simplifying the latest STEM breakthroughs for all.
Highlights: Researchers Arastu Sharma and Rakesh Jain propose a portable, low-cost diagnostic device for detecting Tuberculosis (TB) and multi-drug resistant TB. The device integrates machine learning algorithms with nucleic acid…
Highlights: Researchers introduce a deep learning-powered holographic polarization microscope. Quantitative birefringence data extracted from a single polarization image using AI. Simplifies optical design—requires only one polarizer/analyzer pair. Achieves results comparable…
Highlights: Introduces a weakly supervised deep learning framework for prostate tissue microarray (TMA) classification. Uses Graph Convolutional Networks (GCNs) to model spatial cell organization and improve Gleason grading accuracy. Self-supervised…
Highlights: Researchers use synthetic image rendering to solve the data annotation bottleneck in deep learning. Achieves segmentation accuracy comparable to human-labeled datasets for metal-oxide nanoparticles. Opens new possibilities for automated…
Highlights: Researchers present a deep learning model for automatic heart abnormality detection using heart sound signals. The system combines raw heart sound input with MFCC features using a dual stream…
Highlights: New fusion-based method can generate high-specific-activity radioisotopes essential for medicine. Utilizes high-energy neutrons from deuterium-tritium (D-T) fusion to drive transmutation reactions. Offers a non-fission, proliferation-resistant alternative to reactor-based isotope…
Highlights: PLUTO-4 introduces frontier-scale foundation models for digital pathology. Features two architectures: PLUTO-4S for efficiency and PLUTO-4G for maximal precision. Trained on over 551,000 whole-slide images (WSIs) from more than…
Highlights: Researchers develop a new randomized search model integrating social learning with area-restricted search (ARS). Findings reveal a trade-off between group efficiency, temporal stability, and equity in resource distribution. Optimal…
Highlights: Introduces ‘Modules of Influence’ (MoI) for analyzing model explanation graphs. Uses community detection algorithms to identify groups of interacting features. Improves model interpretability, debugging, and bias localization. Offers open-source…
Highlights: Researchers introduce ‘Modules of Influence’ (MoI), a framework combining graph analysis with model explainability. MoI constructs model explanation graphs and applies community detection to expose interacting feature modules. The…