Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Publications

Publications by Inês Dutra

2011

T-SPPA: Trended Statistical PreProcessing Algorithm

Authors
Silva, T; Dutra, I;

Publication
DIGITAL INFORMATION PROCESSING AND COMMUNICATIONS, PT 1

Abstract
Traditional machine learning systems learn from non-relational data but in fact most of the real world data is relational. Normally the learning task is done using a single flat file, which prevents the discovery of effective relations among records. Inductive logic programming and statistical relational learning partially solve this problem. In this work, we resource to another method to overcome this problem and propose the T-SPPA: Trended Statistical PreProcessing Algorithm, a preprocessing method that translates related records to one single record before learning. Using different kinds of data, we compare our results when learning with the transformed data with results produced when learning from the original data to demonstrate the efficacy of our method.

2011

Predicting Malignancy from Mammography Findings and Surgical Biopsies

Authors
Ferreira, P; Fonseca, NA; Dutra, I; Woods, R; Burnside, E;

Publication
2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM 2011)

Abstract
Breast screening is the regular examination of a woman's breasts to find breast cancer earlier. The sole exam approved for this purpose is mammography. Usually, findings are annotated through the Breast Imaging Reporting and Data System (BIRADS) created by the American College of Radiology. The BIRADS system determines a standard lexicon to be used by radiologists when studying each finding. Although the lexicon is standard, the annotation accuracy of the findings depends on the experience of the radiologist. Moreover, the accuracy of the classification of a mammography is also highly dependent on the expertise of the radiologist. A correct classification is paramount due to economical and humanitarian reasons. The main goal of this work is to produce machine learning models that predict the outcome of a mammography from a reduced set of annotated mammography findings. In the study we used a data set consisting of 348 consecutive breast masses that underwent image guided or surgical biopsy performed between October 2005 and December 2007 on 328 female subjects. The main conclusions are threefold: (1) automatic classification of a mammography, independent on information about mass density, can reach equal or better results than the classification performed by a physician; (2) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) a machine learning model can predict mass density with a quality as good as the specialist blind to biopsy, which is one of our main contributions. Our model can predict malignancy in the absence of the mass density attribute, since we can fill up this attribute using our mass density predictor.

2011

Integrating machine learning and physician knowledge to improve the accuracy of breast biopsy.

Authors
Dutra, I; Nassif, H; Page, D; Shavlik, J; Strigel, RM; Wu, Y; Elezaby, ME; Burnside, E;

Publication
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

Abstract
In this work we show that combining physician rules and machine learned rules may improve the performance of a classifier that predicts whether a breast cancer is missed on percutaneous, image-guided breast core needle biopsy (subsequently referred to as "breast core biopsy"). Specifically, we show how advice in the form of logical rules, derived by a sub-specialty, i.e. fellowship trained breast radiologists (subsequently referred to as "our physicians") can guide the search in an inductive logic programming system, and improve the performance of a learned classifier. Our dataset of 890 consecutive benign breast core biopsy results along with corresponding mammographic findings contains 94 cases that were deemed non-definitive by a multidisciplinary panel of physicians, from which 15 were upgraded to malignant disease at surgery. Our goal is to predict upgrade prospectively and avoid surgery in women who do not have breast cancer. Our results, some of which trended toward significance, show evidence that inductive logic programming may produce better results for this task than traditional propositional algorithms with default parameters. Moreover, we show that adding knowledge from our physicians into the learning process may improve the performance of the learned classifier trained only on data.

1995

Distributing and- and or-work in the Andorra-I parallel logic programming system

Authors
Dutra, IdC;

Publication
British Library, EThOS

Abstract

1996

Distributing And-Work and Or-Work in Parallel Logic Programming Systems

Authors
Dutra, IdC;

Publication
29th Annual Hawaii International Conference on System Sciences (HICSS-29), January 3-6, 1996, Maui, Hawaii, USA

Abstract

2003

Applying Scheduling by Edge Reversal to Constraint Partitioning

Authors
Pereira, MR; Vargas, PK; França, FMG; Castro, MCSd; Dutra, IdC;

Publication
15th Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2003), 10-12 November 2003, Sao Paulo, Brazil

Abstract

  • 10
  • 18