Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation

By Yuqi Fang, Cheng Chen, Yixuan Yuan, R. Tong. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019

419 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8cd4364347f647f2d2165953988c8895524dd8cc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Deng-Ping Fan
2. Yizhou Yu
3. Wenhai Wang
4. Ling Shao
5. Chen Gong
6. Guanbin Li
7. Tao Zhou
8. Lishan Qiao

In Oncology

1. Deng-Ping Fan
2. Huan Fu
3. Ange Lou
4. Murray H. Loew
5. Shuyue Guan
6. Yiwen Hu
7. Shuguang Cui
8. Nima Tajbakhsh

In Radiology, Nuclear Medicine and Imaging

1. Deng-Ping Fan
2. Ling Shao
3. Huan Fu
4. Tao Zhou
5. Guanbin Li
6. Yizhou Yu
7. Songcan Chen
8. Chenxi Duan

Citations in the corpus

• Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (2021, methodology): “We collect several open source models from the field of polyp segmentation, for a total of nine comparative models, including U-Net [4], UNet++ [22], PraNet [5], SFA [80], MSEG [33], ACSNet [47], DCRNet [36], EUNet [50] and SANet [9].”
• Adaptive Context Selection for Polyp Segmentation (2020, methodology): “We compare our ACSNet with FCN8s [1], UNet [14], UNet++ [21], SegNet [19] and SFANet [3] on the test set.”
• Shallow Attention Network for Polyp Segmentation (2021, methodology): “Furthermore, SFA [6] explicitly applies an area-boundary constraint to supervise the learning of both polyp regions and boundaries.”
• Pattern Recognition (2018, positive): “In [14] , generative adversarial network (GAN) was used to generate invariant side-view gait images in normal clothing.”
• Cross-level Feature Aggregation Network for Polyp Segmentation (2023, methodology): “To overcome this problem, Fang et al. [11] designed a boundary-sensitive loss to introduce area-boundary constraints for producing more precise predictions.”
• UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019, methodology): “UNet++ has since been quickly adopted by the research community, either as a strong baseline for comparison [52], [53], [54], [55], or as a source of inspiration for developing newer semantic segmentation architectures [56], [57], [58], [59], [60], [61]; it has also been utilized for multiple…”
• CaraNet: context axial reverse attention network for segmentation of small medical objects (2021, methodology): “We compare our CaraNet with six state-of-the-art medical image segmentation methods: UNet [1], U-Net++ [2], ResUNetmod [15], ResUNet++ [3], SFA [16] and PraNet [4].”