T
Teresa Araújo
Researcher at University of Porto
Publications - 28
Citations - 1699
Teresa Araújo is an academic researcher from University of Porto. The author has contributed to research in topics: Segmentation & Breast cancer. The author has an hindex of 10, co-authored 28 publications receiving 1065 citations. Previous affiliations of Teresa Araújo include University of Twente & Faculdade de Engenharia da Universidade do Porto.
Papers
More filters
Journal ArticleDOI
Classification of breast cancer histology images using Convolutional Neural Networks
Teresa Araújo,Guilherme Aresta,Eduardo Castro,José Rouco,Paulo Aguiar,Catarina Eloy,António Polónia,Aurélio Campilho +7 more
TL;DR: A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed and the sensitivity of the method for cancer cases is 95.6%.
Journal ArticleDOI
BACH: Grand challenge on breast cancer histology images.
Guilherme Aresta,Teresa Araújo,Scotty Kwok,Sai Saketh Chennamsetty,Mohammed Safwan,Varghese Alex,Bahram Marami,Marcel Prastawa,Monica Chan,Michael J. Donovan,Gerardo Fernandez,Jack Zeineh,Matthias Kohl,Christoph Walz,Florian Ludwig,Stefan Braunewell,Maximilian Baust,Quoc Dang Vu,Minh Nguyen Nhat To,Eal Kim,Jin Tae Kwak,Sameh Galal,Veronica Sanchez-Freire,Nadia Brancati,Maria Frucci,Daniel Riccio,Yaqi Wang,Lingling Sun,Kaiqiang Ma,Jiannan Fang,Ismael Kone,Lahsen Boulmane,Aurélio Campilho,Catarina Eloy,António Polónia,Paulo Aguiar +35 more
TL;DR: The Grand Challenge on Breast Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018) as mentioned in this paper.
Journal ArticleDOI
IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
Prasanna Porwal,Prasanna Porwal,Samiksha Pachade,Manesh Kokare,Girish Deshmukh,Jaemin Son,Woong Bae,Lihong Liu,Jianzong Wang,Xinhui Liu,Liangxin Gao,Tian Bo Wu,Jing Xiao,Fengyan Wang,Baocai Yin,Yunzhi Wang,Gopichandh Danala,Linsheng He,Yoon-Ho Choi,Yeong Chan Lee,Sang Hyuk Jung,Zhongyu Li,Xiaodan Sui,Junyan Wu,Xiaolong Li,Ting Zhou,Janos Toth,Agnes Baran,Avinash Kori,Sai Saketh Chennamsetty,Mohammed Safwan,Varghese Alex,Xingzheng Lyu,Li Cheng,Qinhao Chu,Pengcheng Li,Xin Ji,Sanyuan Zhang,Shen Yaxin,Ling Dai,Oindrila Saha,Rachana Sathish,Tânia Melo,Teresa Araújo,Balazs Harangi,Bin Sheng,Ruogu Fang,Debdoot Sheet,Andras Hajdu,Yuanjie Zheng,Ana Maria Mendonça,Shaoting Zhang,Aurélio Campilho,Bin Zheng,Dinggang Shen,Luca Giancardo,Gwenole Quellec,Fabrice Meriaudeau +57 more
TL;DR: The set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD), which received a positive response from the scientific community, have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
Journal ArticleDOI
CATARACTS: Challenge on automatic tool annotation for cataRACT surgery
Hassan Al Hajj,Mathieu Lamard,Pierre-Henri Conze,Soumali Roychowdhury,Xiaowei Hu,Gabija Maršalkaitė,Odysseas Zisimopoulos,Muneer Ahmad Dedmari,Fenqiang Zhao,Jonas Prellberg,Manish Sahu,Adrian Galdran,Teresa Araújo,Duc My Vo,Chandan Panda,Navdeep Dahiya,Satoshi Kondo,Zhengbing Bian,Arash Vahdat,Jonas Bialopetravičius,Evangello Flouty,Chenhui Qiu,Sabrina Dill,Anirban Mukhopadhyay,Pedro Alves Costa,Guilherme Aresta,Senthil Ramamurthy,Sang-Woong Lee,Aurélio Campilho,Stefan Zachow,Shunren Xia,Sailesh Conjeti,Danail Stoyanov,Jogundas Armaitis,Pheng-Ann Heng,William G. Macready,Béatrice Cochener,Gwenole Quellec +37 more
TL;DR: Evaluating tool annotation algorithms based on deep learning for cataract surgery finds that the quality of their annotations are compared to that of human interpretations, and it is expected that they will guide the design of efficient surgery monitoring tools in the near future.
Journal ArticleDOI
DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
Teresa Araújo,Guilherme Aresta,Luís Mendonça,Susana Penas,Carolina Maia,Ângela Carneiro,Ana Maria Mendonça,Aurélio Campilho +7 more
TL;DR: This paper proposed DR|GRADUATE, a deep learning-based DR grading CAD system that supports its decision by providing a medically interpretable explanation and an estimation of how uncertain that prediction is, allowing the ophthalmologist to measure how much that decision should be trusted.