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002
Publications

2023

Symmetry-based regularization in deep breast cancer screening

Authors
Castro, E; Pereira, JC; Cardoso, JS;

Publication
MEDICAL IMAGE ANALYSIS

Abstract
Breast cancer is the most common and lethal form of cancer in women. Recent efforts have focused on developing accurate neural network-based computer-aided diagnosis systems for screening to help anticipate this disease. The ultimate goal is to reduce mortality and improve quality of life after treatment. Due to the difficulty in collecting and annotating data in this domain, data scarcity is - and will continue to be - a limiting factor. In this work, we present a unified view of different regularization methods that incorporate domain-known symmetries in the model. Three general strategies were followed: (i) data augmentation, (ii) invariance promotion in the loss function, and (iii) the use of equivariant architectures. Each of these strategies encodes different priors on the functions learned by the model and can be readily introduced in most settings. Empirically we show that the proposed symmetry-based regularization procedures improve generalization to unseen examples. This advantage is verified in different scenarios, datasets and model architectures. We hope that both the principle of symmetry-based regularization and the concrete methods presented can guide development towards more data-efficient methods for breast cancer screening as well as other medical imaging domains.

2023

Fill in the blank for fashion complementary outfit product Retrieval: VISUM summer school competition

Authors
Castro, E; Ferreira, PM; Rebelo, A; Rio-Torto, I; Capozzi, L; Ferreira, MF; Goncalves, T; Albuquerque, T; Silva, W; Afonso, C; Sousa, RG; Cimarelli, C; Daoudi, N; Moreira, G; Yang, HY; Hrga, I; Ahmad, J; Keswani, M; Beco, S;

Publication
MACHINE VISION AND APPLICATIONS

Abstract
Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

2020

Soft Rotation Equivariant Convolutional Neural Networks

Authors
Castro, E; Pereira, JC; Cardoso, JS;

Publication
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

Abstract

2019

DEEP KEYPOINT DETECTION FOR THE AESTHETIC EVALUATION OF BREAST CANCER SURGERY OUTCOMES

Authors
Silva, W; Castro, E; Cardoso, MJ; Fitzal, F; Cardoso, JS;

Publication
2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)

Abstract
Breast cancer high survival rate led to an increased interest in the quality of life after treatment, particularly regarding the aesthetic outcome. Currently used aesthetic assessment methods are subjective, which make reproducibility and impartiality impossible. To create an objective method capable of being selected as the gold standard, it is fundamental to detect, in a completely automatic manner, keypoints in photographs of women's torso after being subjected to breast cancer surgeries. This paper proposes a deep and a hybrid model to detect keypoints with high accuracy. Our methods are tested on two datasets, one composed of images with a clean and consistent background and a second one that contains photographs taken under poor lighting and background conditions. The proposed methods represent an improvement in the detection of endpoints, nipples and breast contour for both datasets in terms of average error distance when compared with the current state-of-the-art.

2019

Weight Rotation as a Regularization Strategy in Convolutional Neural Networks

Authors
Castro, E; Pereira, JC; Cardoso, JS;

Publication
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Abstract