Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging. [PDF]
Doan LMT +7 more
europepmc +1 more source
Super-resolution biomedical imaging via reference-free statistical implicit neural representation. [PDF]
Ye S, Shen L, Islam MT, Xing L.
europepmc +1 more source
Creating Ti–Fe α/β Alloys by Diffusion‐Driven Solid‐State Processing
This study proposes making alloys containing fast diffusing elements that are difficult to produce by ingot metallurgy, by diffusion‐driven solid‐state HIP processing of elemental powders and low‐temperature homogenisation. Here, novel Fe‐Ti α–β alloys are formed having fine α–β lamellae, a small β prior grain size without significant intermetallics ...
Jiaqi Xu +10 more
wiley +1 more source
Biomedical Imaging: 2001 and Beyond [PDF]
D S, Lester, J L, Olds
openaire +2 more sources
Hydrogel metapad with ultrasound transparency and broadband focusing for biomedical imaging. [PDF]
Zhang J +12 more
europepmc +1 more source
Persistent luminescent nanophosphors for applications in cancer theranostics, biomedical, imaging and security. [PDF]
Mushtaq U +7 more
europepmc +1 more source
Mg–Zn composites with a thickness of 0.21 mm were fabricated using roll bonding of a kirigami‐patterned Mg alloy inlay within a Zn matrix. Thermal activation following this process led to the formation of tailored intermetallic structures, which provided the composite with enhanced flexural strength.
Yaroslav Frolov +4 more
wiley +1 more source
Applications of Nanomaterials in Biomedical Imaging and Cancer Therapy: 3rd Edition. [PDF]
Chow JCL.
europepmc +1 more source
Enhanced Strength and Corrosion Resistance of Ti‐13Nb‐12Ta‐10Zr‐4Sn Alloy by Aging Treatment
This work systematically investigates the effect of aging treatment on mechanical properties and corrosion behavior of vacuum arc‐melted Ti‐13Nb‐12Ta‐10Zr‐4Sn alloy. Owing to the increased α″ martensite, strength and corrosion resistance were significantly enhanced by aging treatment.
Yuhua Li +5 more
wiley +1 more source
Deep learning for deep learning performance: How much data is needed for segmentation in biomedical imaging? [PDF]
Lee J, Chung H, Suh M, Lee JH, Choi KS.
europepmc +1 more source

