Results 221 to 230 of about 106,507 (285)

RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells

FEBS Open Bio, EarlyView.
RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour, Christine Volmert, Valentina Piano   +2 more
wiley   +1 more source

Discriminant Functions

The Annals of Mathematical Statistics, 1947
openaire   +3 more sources

Comparative evaluation of generative artificial intelligence models for synthetic knee radiograph augmentation in clinical research. [PDF]

BMC Med Imaging
Chung K, Nam JH, Dosset A, Koh YG, Kim JM, Kim PS, Lee JW, Park KM, Kwon HM, Kang KT.   +9 more
europepmc   +1 more source

Multiscale attention generative adversarial networks for lesion synthesis in chest X-ray images. [PDF]

Sci Rep
Alam MS, Meijering E, Wang D, Sowmya A.
europepmc   +1 more source

Self-supervised AI for decoding and designing disordered metamaterials. [PDF]

Sci Adv
Shen M, Liu K, Mao S, Daraio C.
europepmc   +1 more source

Knit-Pix2Pix: An Enhanced Pix2Pix Network for Weft-Knitted Fabric Texture Generation. [PDF]

Sensors (Basel)
Ru X, Huang Y, Peng L, Hou Y.
europepmc   +1 more source

Enhanced Magnetic Resonance Imaging-Based Knee Cartilage Segmentation Using a Swin-UNet Conditional Generative Adversarial Network: Development and Validation Study.

JMIR Med Inform
Park JY, Nam JH, Abdigapporov S, Kim JK, Koh YG, Cho BW, Kwon HM, Park KK, Kang KT.   +8 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Discriminative motifs

Proceedings of the sixth annual international conference on Computational biology, 2002
This paper takes a new view of motif discovery, addressing a common problem in existing motif finders. A motif is treated as a feature of the input promoter regions that leads to a good classifier between these promoters and a set of background promoters. This perspective allows us to adapt existing methods of feature selection, a well-studied topic in
openaire   +2 more sources