breast tumor segmentation in dce-mri using fully convolutional networks with an application in radiogenomics (Optics and Photonics)
90
Structured Review
Optics and Photonics
breast tumor segmentation in dce-mri using fully convolutional networks with an application in radiogenomics
Breast Tumor Segmentation In Dce Mri Using Fully Convolutional Networks With An Application In Radiogenomics, supplied by Optics and Photonics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/segmenter+application/breast+tumor+segmentation+in+dce+mri+using+fully+convolutional+networks+with+an+application+in+radiogenomics/pm34428680-391-15-26
Average 90 stars, based on 1 article reviews
Breast Tumor Segmentation In Dce Mri Using Fully Convolutional Networks With An Application In Radiogenomics, supplied by Optics and Photonics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/segmenter+application/breast+tumor+segmentation+in+dce+mri+using+fully+convolutional+networks+with+an+application+in+radiogenomics/pm34428680-391-15-26
Average 90 stars, based on 1 article reviews
breast tumor segmentation in dce-mri using fully convolutional networks with an application in radiogenomics - by Bioz Stars,
2026-10
90/100 stars
Images
Related Articles
Imaging:Article Title: A Robust Breast ultrasound segmentation method under noisy annotations. Article Snippet: Background and Objective: A large-scale training data and accurate annotations are fundamental for current segmentation networks.. However, the characteristic artifacts of ultrasound images always make the annotation task complicated, such as attenuation, speckle, shadows and signal dropout.. Further complications arise as the contrast between the region of interest and background is often low. Biomarker Discovery:Article Title: A Robust Breast ultrasound segmentation method under noisy annotations. Article Snippet: Background and Objective: A large-scale training data and accurate annotations are fundamental for current segmentation networks.. However, the characteristic artifacts of ultrasound images always make the annotation task complicated, such as attenuation, speckle, shadows and signal dropout.. Further complications arise as the contrast between the region of interest and background is often low. |
