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-field imaging of dynamic processes" "Multi-scale X-ray speckle-based imaging" "Spectral X-ray speckle-based imaging" "Single-shot multi-projection X-ray phase-contrast imaging" "X-ray virtual histology
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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The project involves building and curating a comprehensive food image dataset suitable for mobile AI applications. High-accuracy deep learning models will be trained on this dataset and then
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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random but can only happen along particular directions. However, as the material is made up of many crystals, and they all have different orientations, the deformation process of a polycrystalline material
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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where
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include: 1. Identify a novel co-design methodology along with the structural templates that could be used to drive the further design process of collocated teamwork analytics with critical educational
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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will work with advanced structural biology and imaging technologies, including single particle cryogenic electron microscopy (cryoEM), cryogenic electron tomography (cryoET), and cryogenic plasma focused
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Quantum optics Quantum energy Nanophotonics for imaging applications Electron-beam induced nanophotonic phenomena Photonics of two-dimensional materials All are active areas of research in the School