Results 201 to 210 of about 17,012 (262)

Interferon beta drives therapy resistance in a patient‐derived model of high‐grade serous ovarian cancer

Molecular Oncology, EarlyView.
Interferon type 1 (IFN‐1) production and signaling is associated with the acquisition of therapy resistance, following chronic DNA damage, via Interferon‐related DNA damage resistance signature (IRDS) gene expression. An alternative, DNA damage‐independent role of sustained IFN‐1 mediated resistance was identified and characterized by the emergence of ...
Ashlyn Conant, Tise Suzuki, Kiera McGivney, V. S. S. Abhinav Ayyadevara, Sharon Asariah, Jay Deng, Ethan Nyein, Jacqueline Coats, Gary Yu, Yevgeniya J. Ioffe, Christian Hurtz, Juli J. Unternaehrer   +11 more
wiley   +1 more source

MuGu:mutual guidance learning between pretrained SAM and lightweight model for medical image segmentation. [PDF]

Sci Rep
Wang C, Wang Z, Chen W, Zou J, Zhuang Y.
europepmc   +1 more source

FQGR-net: Morphology-based litchi flower quantification and gender recognition. [PDF]

Plant Phenomics
Li J, Ma Z, Lin J, Wang Q, Lin X, Xia J, Xin B.   +6 more
europepmc   +1 more source

KT-YOLO: A multi-convolution kernel collaboration model for dense Hu sheep behavior detection. [PDF]

PLoS One
Zhang S, Chang H, Wu Z, Wu G, Ji R.
europepmc   +1 more source

Oblique-view video tracking and density-based counting: accurate counting of late-stage rapeseed seedlings for breeding assessment. [PDF]

Front Plant Sci
Luo B, Yang Y, Zhang K, Lv X, Liu Y, Yang Y, Zhang F, Liu L, Zhang G, Wang X, Wu Z.   +10 more
europepmc   +1 more source

Localized Query Attack Toward Transformer-Based Visible Object Detectors. [PDF]

Sensors (Basel)
Wang Y, Li A, Yang Z, Liu X.
europepmc   +1 more source

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Foreground-focused domain adaption for object detection

2020 25th International Conference on Pattern Recognition (ICPR), 2021
Object detectors suffer from accuracy loss caused by domain shift from a source to a target domain. Unsupervised domain adaptation (UDA) approaches mitigate this loss by training with unlabeled target domain images. A popular processing pipeline applies adversarial training that aligns the distributions of the features from the two domains. We advocate
Yuchen Yang, Nilanjan Ray
openaire   +1 more source

Fractal-based analysis for foreground detection

2015 49th Asilomar Conference on Signals, Systems and Computers, 2015
We have developed a fractal-based analysis technique for use with foreground detection. In this technique, a modified form of the box-counting fractal dimension is used to identify meaningful structures when looking at pixels which change between frames in a video sequence.
Daniel Raburn, Edward R. Ratner
openaire   +1 more source

The effect of noise on foreground detection algorithms

Artificial Intelligence Review, 2016
Background segmentation methods are exposed to the effects of different kinds of noise due to the limitations of image acquisition devices. This type of distortion can worsen the performance of segmentation methods because the input pixel values are altered.
Francisco Javier López-Rubio, Ezequiel López-Rubio, Miguel A. Molina-Cabello, Rafael Marcos Luque Baena, Esteban J. Palomo, Enrique Domínguez   +5 more
openaire   +1 more source