Lightweight Deep Learning Pipeline for Detection, Segmentation and Classification of Breast Cancer Anomalies

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Oliveira, HS (Autor) Outra; Teixeira, JF (Autor) Outra; Oliveira, HP (Autor) FCUP

Ata de Conferência Internacional

Título: IMAGE ANALYSIS AND PROCESSING - ICIAP 2019, PT II

Páginas: 707-715

20th International Conference on Image Analysis and Processing (ICIAP)

Univ Trento, Fac Law, Trento, ITALY, SEP 09-13, 2019

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 8 Citações

Outras Informações

ID Authenticus: P-00R-4FX

DOI: 10.1007/978-3-030-30645-8_64

Abstract (EN): The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods. Results are obtained using the INbreast public database in the context of lesion detection and BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network using ResNet50 is modified to generate the lesion regions proposals followed by a false positive reduction and contour refinement stages while a pre-trained VGG16 network is fine-tuned to classify mammograms. The detection and segmentation stage results show that the cascade configuration achieves a DICE of 0.83 without massive training while the multi-class classification exhibits an MAE of 0.58 with data augmentation.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9