Deep Learning Framework Classifies and Counts Mineral Grains Without Segmentation Labels
A new deep learning framework segments, classifies and counts microscopic mineral grains with 97.57% accuracy without requiring annotated segmentation masks.
A new deep learning framework segments, classifies and counts microscopic mineral grains with 97.57% accuracy without requiring annotated segmentation masks.
Whole-exome sequencing of men from Kerala's high background radiation areas found no excess germline mutations compared with matched controls, despite ...
Alluvial heavy mineral deposits in central Portugal's Castelo Branco Massif reveal anomalously high concentrations of rare earth elements, titanium, and ...
An optimized species distribution model projects that China's rare earth hyperaccumulator fern Blechnopsis orientalis will expand its range northwestward under ...
A newly analysed carbonatite plug at Khaderpet in southern India is exceptionally enriched in light rare earth elements and occurs ...
A new gamma spectrometry survey of Visakhapatnam's beaches reveals natural radionuclide concentrations exceeding global averages, with thorium-rich hotspots comparable to ...
An integrated geochemical, radiological and statistical study of Kerala's monazite-rich coastal sediments shows that clay content is the key control ...
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© 2025 Scienmag - Science Magazine