TY  - CONF
AU  - Yael Tudela
AU  - Ana Garcia Rodriguez
AU  - Gloria Fernandez Esparrach
AU  - Jorge Bernal
A2  - MICCAIW
PY  - 2023//
TI  - Towards Fine-Grained Polyp Segmentation and Classification
T2  - LNCS
BT  - Workshop on Clinical Image-Based Procedures
SP  - 32
EP  - 42
VL  - 14242
KW  - Medical image segmentation
KW  - Colorectal Cancer
KW  - Vision Transformer
KW  - Classification
N2  - Colorectal cancer is one of the main causes of cancer death worldwide. Colonoscopy is the gold standard screening tool as it allows lesion detection and removal during the same procedure. During the last decades, several efforts have been made to develop CAD systems to assist clinicians in lesion detection and classification. Regarding the latter, and in order to be used in the exploration room as part of resect and discard or leave-in-situ strategies, these systems must identify correctly all different lesion types. This is a challenging task, as the data used to train these systems presents great inter-class similarity, high class imbalance, and low representation of clinically relevant histology classes such as serrated sessile adenomas.In this paper, a new polyp segmentation and classification method, Swin-Expand, is introduced. Based on Swin-Transformer, it uses a simple and lightweight decoder. The performance of this method has been assessed on a novel dataset, comprising 1126 high-definition images representing the three main histological classes. Results show a clear improvement in both segmentation and classification performance, also achieving competitive results when tested in public datasets. These results confirm that both the method and the data are important to obtain more accurate polyp representations.
UR  - https://doi.org/10.1007/978-3-031-45249-9_4
UR  - http://dx.doi.org/10.1007/978-3-031-45249-9_4
N1  - ISE
ID  - Yael Tudela2023
ER  -