Abstracb-Submicron CO wires with a variable width in the range from 0.2 to 2 pm and paired CO disks with a ameter in the range from 0.4 to 5 p m were o investigate the magnetoresistance effect in with the domain wall formation. A clear change in resist~~ty due to traversing domain wall along the wire the widths in both longitudinal and transverse magnetoresistance effects. The domain wall gives a negative contr~bution to the magnetoresistance effect. ~o n g i t u d i n~ magnetoresistance effects of paired CO disks show an anomalous increase in low fields below 20 kOe due to the peculiar vortex structure stabilized in the disk.
Arrays of s u b~c~n Go rectangular dots with the aspect ratio ranging from 1 to 4 and antidots with the tion measurements show that Go dots are well controlled hereas the antidots exhibit field d i~c~i o n .
Tissue phenotyping of the tumor microenvironment has a decisive role in digital profiling of intra-tumor heterogeneity, epigenetics, and progression of cancer. Most of the existing methods for tissue phenotyping often rely on time-consuming and error-prone manual procedures. Recently, with the advent of advanced technologies, these procedures have been automated using artificial intelligence techniques. In this paper, a novel deep histology heterogeneous feature aggregation network (HHFA-Net) is proposed based on visual and semantic information fusion for the detection of tissue phenotypes in colorectal cancer (CRC). We adopted and tested various data augmentation techniques to avoid computationally expensive stain normalization procedures and handle limited and imbalanced data problems. Three publicly available datasets are used in the experiments: CRC tissue phenotyping (CRC-TP), CRC histology (CRCH), and colon cancer histology (CCH). The proposed HHFA-Net achieves higher accuracies than the state-of-the-art methods for tissue phenotyping in CRC histopathology images.
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