Name
Title | ||
|---|---|---|
Global Bank: A Guided Pathway of Encoding and Decoding for Pathological Image Analysis |
Abstract | ||
|---|---|---|
The encoder-decoder architecture of convolutional neural networks (CNNs) is widely used in computer vision tasks and various analyses of medical images. However, extracting semantic features from regions of interest (RoIs) in pathological images remains a challenging task because RoIs of different morphologies and scales are embedded in a blurred background. Additionally, it is well known that the classic encoder-decoder architecture is vulnerable to interference from a blurred background and is thus not entirely suitable for precise analysis of pathological images. In this paper, we propose a pathway named global bank (GLB) to guide the encoder and decoder to focus more on the RoIs by providing the decoder with additional effective features of the RoIs. We extend the U-Net and feature pyramid network (FPN) with GLB and evaluate the resulting models on gland segmentation and cancer embolus detection tasks, respectively. Extensive experiments demonstrate that our proposal can significantly improve the performance of the encoder-decoder architecture. The U-Net with GLB achieves the best semantic segmentation performance on the 2015 MICCAI Gland Challenge dataset. Additionally, the FPN with GLB achieves improvements of 2% in average precision and 3.4% in recall on the embolus detection task. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBM47256.2019.8983091 | 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
Global Bank,Encoder,Decoder,CNN,RoI | Pattern recognition,Computer science,Convolutional neural network,Segmentation,Artificial intelligence,Pyramid,Encoder,Decoding methods,Recall,Machine learning,Encoding (memory) | Conference |
ISBN | Citations | PageRank |
978-1-7281-1868-0 | 0 | 0.34 |
References | Authors | |
11 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hansheng Li | 1 | 0 | 2.37 |
| Jun Feng | 2 | 44 | 16.44 |
| Baosheng Kang | 3 | 0 | 0.68 |
| Yuxin Kang | 4 | 0 | 2.37 |
| Feihong Liu | 5 | 0 | 0.34 |
| Wenli Hui | 6 | 0 | 0.68 |
| Qirong Bo | 7 | 0 | 1.35 |
| Chunbao Wang | 8 | 0 | 0.34 |
| Lin Yang | 9 | 1291 | 116.88 |
| Lizhen Cui | 10 | 154 | 38.68 |