The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The Importance of Skip Connections in Biomedical Image Segmentation
Abstract
In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.
Overview
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Self-citationBrosch, T., Tang, L. Y. W., Yoo, Y., Li, D. K. B., Traboulsee, A., & Tam, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528821 · Cited by 460
Cited by
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477
NAS-Unet: Neural Architecture Search for Medical Image Segmentation
Weng, Y., Zhou, T., Li, Y., & Qiu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908991 · Cited by 487
“[31] make a different between long skip connections (which means a skip connection between two feature maps that are far away) and short skip connections (which usually called single residual block) and find that both of them to be beneficial in creating deep architectures for medical image segmentation.”
BackgroundFederated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394
Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis
Natekar, P., Kori, A., & Krishnamurthi, G. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00006 · Cited by 103
Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84
Xu, L., Liu, M., Shen, Z., Wang, H., Liu, X., Wang, X., Wang, S., Li, T., Yu, S., Hou, M., et al. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101690 · Cited by 87
Chlebus, G., Meine, H., Thoduka, S., Abolmaali, N., van Ginneken, B., Hahn, H. K., & Schenk, A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217228 · Cited by 56
Pati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., et al. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9449 · Cited by 51
Endometrium segmentation on transvaginal ultrasound image using key-point discriminator
Park, H., Lee, H. J., Kim, H. G., Ro, Y. M., Shin, D., Lee, S. R., Kim, S. H., & Kong, M. (2019) · Medical Physics · doi:10.1002/mp.13677 · Cited by 37
Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation
Ravishankar, H., Thiruvenkadam, S., Venkataramani, R., & Vaidya, V. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_49 · Cited by 23
MediSee: Reasoning-based Pixel-level Perception in Medical Images
Tong, Q., Lu, Z., Liu, J., Zheng, Y., & Lu, Z. (2025) · arXiv · doi:10.48550/arxiv.2504.11008 · Cited by 3
QCResUNet: Joint Subject-Level and Voxel-Level Prediction of Segmentation Quality
Qiu, P., Chakrabarty, S., Nguyen, P., Ghosh, S. S., & Sotiras, A. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-43901-8_17 · Cited by 3
Metadata
- Authors
- Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., & Pal, C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 179-187
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2517954747
- Cited by
- 984
- References
- 6
- FWCI
- 20.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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.
