با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning
ترجمه فارسی عنوان مقاله:
تشخیص توده سینه از ماموگرافی های دیجیتالی شده اشعه ایکس بر اساس ترکیبی از یادگیری عمیق فعال و یادگیری خود گام
منبع:
Sciencedirect - Elsevier - Future Generation Computer Systems, 101 (2019) 668-679: doi:10:1016/j:future:2019:07:013
نویسنده:
Rongbo Shen a,1, Kezhou Yan b, Kuan Tian b, Cheng Jiang b, Ke Zhou a,∗
چکیده انگلیسی:
Breast mass detection is a challenging task in mammogram, since mass is usually embedded and
surrounded by various normal tissues with similar density. Recently, deep learning has achieved
impressive performance on this task. However, most deep learning methods require large amounts of
well-annotated datasets. Generally, the training datasets is generated through manual annotation by
experienced radiologists. However, manual annotation is very time-consuming, tedious and subjective.
In this paper, for the purpose of minimizing the annotation efforts, we propose a novel learning
framework for mass detection that incorporates deep active learning (DAL) and self-paced learning
(SPL) paradigm. The DAL can significantly reduce the annotation efforts by radiologists, while improves
the efficiency of model training by obtaining better performance with fewer overall annotated samples.
The SPL is able to alleviate the data ambiguity and yield a robust model with generalization capability
in various scenarios. In detail, we first employ a few of annotated easy samples to initialize the
deep learning model using Focal Loss. In order to find out the most informative samples, we propose
an informativeness query algorithm to rank the large amounts of unannotated samples. Next, we
propose a self-paced sampling algorithm to select a number of the most informative samples. Finally,
the selected most informative samples are manually annotated by experienced radiologists, which
are added into the annotated samples for the model updating. This process is looped until there
are not enough most informative samples in the unannotated samples. We evaluate the proposed
learning framework on 2223 digitized mammograms, which are accompanied with diagnostic reports
containing weakly supervised information. The experimental results suggest that our proposed learning
framework achieves superior performance over the counterparts. Moreover, our proposed learning
framework dramatically reduces the requirement of the annotated samples, i.e., about 20% of all
training data.
Keywords: Breast cancer | Mammography | Mass detection | Deep active learning | Self-paced learning
قیمت: رایگان
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