The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Road Extraction by Deep Residual U-Net
Zhang, Z., Liu, Q., & Wang, Y. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2802944 · Cited by 3,179
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.02.007 · Cited by 2,191
Cited by
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259
“, OCR [67, 65, 68], 3D [27, 10, 26] and medical [14, 15, 28] image analysis) to be explored in the future, and hope that PVT could serve as a good starting point.”
BackgroundSelf-citationInf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
Self-citationRGB-D salient object detection: A survey
Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331
Self-citationFocus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
Yeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104815 · Cited by 186
Distribution-free, Risk-controlling Prediction Sets
Bates, S., Angelopoulos, A., Lei, L., Malik, J., & Jordan, M. (2021) · Journal of the ACM · doi:10.1145/3478535 · Cited by 144
“We used PraNet [54] as our base segmentation model.”
MethodColorectal polyp detection in colonoscopy images using YOLO-V8 network
Lalinia, M., & Sahafi, A. (2024) · Signal, Image and Video Processing · doi:10.1007/s11760-023-02835-1 · Cited by 99
Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. H., Verjans, J. W., Singh, R., & Carneiro, G. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87240-3_13 · Cited by 77
Angelopoulos, A. N., Bates, S., Fisch, A., Lei, L., & Schuster, T. (2022) · arXiv · doi:10.48550/arxiv.2208.02814 · Cited by 37
(2025) · doi:10.1007/978-3-032-04965-0_8 · Cited by 6
Liu, S., Bao, L., Yang, Q., Geng, W., Zheng, B., Li, C., Chen, W., Peng, H., & Yuan, Y. (2026) · arXiv · doi:10.48550/arxiv.2602.03320 · Cited by 1
“…et al., 2015), nn-UNet (Isensee et al., 2021), and other representative architectures (Li et al., 2025a; Zhou et al., 2018; Chen et al., 2021; Fan et al., 2020) are inherently task-specific, which require dedicated training for each individual segmentation task, thus exhibiting limited…”
Optimized Certainty Equivalent Risk-Controlling Prediction Sets
Huang, J., Farzaneh, A., & Simeone, O. (2026) · arXiv · doi:10.48550/arxiv.2602.13660
“We adopt a pre-trained PraNet [14] as the base segmentation model p ( y | x ) .”
Controlling False Positives in Image Segmentation via Conformal Prediction
Mossina, L., & Friedrich, C. (2025) · arXiv · doi:10.48550/arxiv.2511.15406
Metadata
- Authors
- Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 263-273
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3092344722
- Cited by
- 1,499
- References
- 3
- FWCI
- 268.83
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.