Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this pap...
BACKGROUND: Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast cancer patients a...
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Feb 13, 2017
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distingu...
OBJECTIVE: We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information o...
International journal of computer assisted radiology and surgery
Feb 6, 2017
PURPOSE: We developed an image-guided intervention robot system that can be operated in a magnetic resonance (MR) imaging gantry. The system incorporates a bendable needle intervention robot for breast cancer patients that overcomes the space limitat...
The computer-aided diagnosis has become one of the major research topics in medical diagnostics. In this research paper, we focus on designing an automated computer diagnosis by combining two major methodologies, namely the fuzzy base systems and the...
We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combi...
IEEE transactions on pattern analysis and machine intelligence
Jan 23, 2017
The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques ...
BACKGROUND: Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to eval...
Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature represent...
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