REVIEW 2 major objections 5 minor 51 references
OCELOT 2023: Cell Detection from Cell-Tissue Interaction Challenge
T0 review · 2 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash
Pith's one-line read Tissue context lifts cell detection by up to 8 F1 points
desk verdict The dataset and subgroup analysis are worth having; the headline causal claim about cell-tissue context outruns the evidence. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the overlapped patch-pair dataset: 673 pairs of a small 1024x1024 cell-detection patch (0.2 microns per pixel) contained within a large 4096x4096 tissue-segmentation patch downsampled to 0.8 microns per pixel. Cell labels are point annotations (tumor cell vs background cell), and tissue labels are pixel-wise (cancer area vs background). The mechanism carrying the argument is the injection of tissue predictions into the cell model's features (via concatenation or element-wise addition during training), which gives the cell detector an explicit prior about its spatial context.
What would settle it
Re-run the winning pipelines with and without tissue context while holding architecture, pretraining, ensembling, and augmentation fixed; if the F1 gap collapses or becomes negligible, tissue context is not the driver of the reported improvement.
Extended reading notes
Core claim
The central claim is that incorporating tissue segmentation information into cell detection models produces consistent, substantial improvements across all cell types and evaluation metrics. The challenge dataset pairs each high-magnification cell patch with a lower-magnification tissue patch that fully contains it, enabling models to learn the correlation between cell types and tissue regions (tumor cells with cancer areas, background cells with benign tissue). The top submissions achieved mean F1 scores of 69.92 to 72.44, compared with 63.54 for the cell-only baseline, and the improvements persisted for both tumor cells and background cells. The paper also reports that atypical pairings, s
Load-bearing premise
The measured F1 gains are attributed to cell-tissue modeling, but the baseline is not matched in architecture, pretraining, ensembling, or training tricks, so those confounds could explain part or all of the gap.
Editorial extensions
If this is right
- If the central claim holds, multi-scale tissue context should become a standard input to cell detection pipelines, not an optional enhancement.
- The precision-dominant improvement suggests tissue priors mainly reduce false positives; clinical workflows that value specificity could benefit most directly.
- The consistent weakness on atypical cell-tissue pairings implies that future work must explicitly balance tissue priors with cell-level appearance to avoid missing clinically important, rare configurations.
- The challenge dataset provides a reusable benchmark for measuring whether any proposed cell-tissue fusion method beats cell-only models on the same test set.
- Methods that fuse tissue information during training appear more effective than post-training heuristic fusion, pointing to where architectural effort should go.
Reading between the lines
- Editorial inference: the 6 to 7.99 point F1 gain could shrink substantially under an architecture-matched baseline, because the comparison is confounded by different backbones, pretrained weights, ensembling, and augmentation policies.
- Editorial inference: the same paired patch structure could support weak-supervision transfer, where tissue segmentation labels pre-train a shared encoder before fine-tuning on sparse cell point labels.
- Editorial inference: the binary tumor/background cell label is coarse; a natural extension is to test whether tissue context helps distinguish finer cell classes such as lymphocytes, which are clinically important inside tumor regions.
- Editorial inference: the dataset's 91.7% and 88.5% co-location rates between cell and tissue classes imply that a simple tissue-prior classifier would already capture much of the signal, so future baselines should control for that prior strength.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper reports the OCELOT 2023 challenge, organized to test whether incorporating cell-tissue relationships improves cell detection in H&E whole-slide images. It introduces a multi-organ dataset of 673 paired cell-detection and tissue-segmentation patches from 306 TCGA WSIs, divides it into train/validation/test subsets, and describes the methods of the five top-scoring participant teams plus the organizers' earlier Ocelot method. The results show that all top submissions outperform a single cell-only DeepLabV3+/ResNet34 baseline by 6–7.99 mean-F1 percentage points, and the paper interprets this as evidence for the cell-tissue hypothesis. The evaluation uses a fixed hidden test set, bootstrapped 95% confidence intervals, and public evaluation code, and the paper follows the BIAS reporting guideline. The central interpretive claim, however, rests on an uncontrolled comparison: the baseline and the submitted methods differ in architecture, pretraining, label encoding, ensembling, test-time augmentation, and training schedules, so the observed gain is not causally attributable to cell-tissue modeling alone.
Significance. If the causal claim were established, the result would be significant for computational pathology, since it would support multi-scale, context-aware cell detection over isolated cell-only models. The dataset itself is a valuable public resource, and the challenge is well executed procedurally: independent external teams, a hidden test set, a fixed evaluation protocol with public code, bootstrap uncertainty estimation, and adherence to BIAS. These are genuine strengths. The weakness is interpretive: the 6–7.99 point F1 advantage is presented as validation of the cell-tissue hypothesis, but the comparison is confounded by architectural and training choices. The paper itself acknowledges in §5 that attribution is challenging. As reported, the evidence supports the weaker statement that the submitted challenge solutions outperform the baseline, not that cell-tissue modeling caused the improvement. The dataset and challenge remain valuable even with that more conservative framing.
major comments (2)
- [§4.1 and Table 3] The main claim—that cell-tissue relationship modeling yields the 6–7.99 point F1 improvement—is not supported by the comparison as run. The cell-only baseline is DeepLabV3+/ResNet34 with disk labels, 300 epochs, dice loss, and no ensemble, while the top teams use different architectures (ViT, SegFormer, DeepLabV3+ with ResNet50), SAM/ImageNet pretraining, ensembles, TTA, and different label representations (disks, Gaussians, NuClick, repel coding). Any of these differences could account for a large share of the gain. The paper concedes in §5 that 'it is challenging to clearly attribute performance differences to specific methodological choices,' yet the Abstract and §4.1 still present the gain as validation of the hypothesis. Please add a controlled comparison (e.g., the top model with the tissue branch disabled, or the baseline with tissue input injected under identical training conditi
- [§3 and Tables 3–4] Ryu et al. (2023) is listed as an included method and Table 3 provides its configuration, but no performance is reported for it in Table 4, Fig. 5, or Fig. 6. Since this is the method that originally proposed cell-tissue injection and the challenge is intended to validate that hypothesis, omitting its test-set results removes the most direct check of the proposed mechanism. Either report the score for this method or explicitly state why it is excluded from the results.
minor comments (5)
- [§4.1 / Fig. 4] The claim that all solutions outperform the baseline 'across all metrics and cell types' should be qualified. For Lo and Yang, the mean-F1 confidence interval [66.17, 73.15] overlaps the baseline upper bound of 66.76, and the TC and BC intervals also overlap. The pairwise bootstrap probabilities in Fig. 6 are stronger evidence; please cite them when making this claim.
- [Throughout] The team name is spelled 'Millward' in the author list and references, but 'Milward' in Fig. 4, Fig. 5, Fig. 6, and some text. Unify the spelling.
- [Table 3] 'Elm.-wise add' in the 'Modeling of cell-tissue relationship' row should be 'element-wise add.' Also, the abbreviations row is dense; define all abbreviations once in the caption for readability.
- [§2.2.3 / §5] The paper states that annotation errors were not quantified. Since the evaluation metric depends on point annotations with a 3 µm match radius, please add a sentence in §5 or the supplementary material on the potential impact of inter-observer variability on the absolute F1 scores and on the stability of the rankings.
- [Figs. 5 and 6] The heatmaps use team labels T1–T6, but the caption does not map them to team names. Please state explicitly that T6 is the cell-only baseline and give the full mapping in the caption.
Circularity Check
No significant circularity; the F1 gains are empirical challenge outcomes, though the comparison is confounded.
full rationale
No circular derivation is present. The paper's central claim—that incorporating cell-tissue context improves cell detection—rests on measured test-set F1 scores from five independent participant teams, compared against an organizers-provided cell-only baseline. The baseline is a genuine model that does not use tissue information, and the participant scores come from a hidden test set with one final submission per team; no parameter is fitted to the test set and then reported as a prediction. The main weakness is experimental confounding: the baseline uses DeepLabV3+/ResNet34, dice loss, no ensemble, and disk labels, while the top teams use different architectures, pretrained weights, ensembles, label encodings, and training schedules. The paper itself acknowledges this in Sec. 5: 'it is challenging to clearly attribute performance differences to specific methodological choices.' That is a causal-attribution limitation, not a circularity: the observed F1 gain is not equal to an input by construction. The repeated citations to Ryu et al. (2023) motivate the dataset and the cell-tissue hypothesis, but the decisive evidence is the externally produced leaderboard result, so the self-citation is not load-bearing in a way that forces the conclusion. No definitional equivalence, fitted-input renaming, or self-citation chain was found.
Assumptions & free parameters
assumptions (3)
- domain assumption Ground truth cell point annotations and tissue segmentation maps are accurate enough to support the reported F1 differences.
- domain assumption The 3 micrometer matching radius is a valid criterion for counting a detection as correct.
- domain assumption The selected high-quality ROIs are representative of the clinical task of cell detection.
Cite this review
Pith. "Pith review of OCELOT 2023: Cell Detection from Cell-Tissue Interaction Challenge." pith.science (2026). https://pith.science/paper/4AQ4OA52
@misc{pith2026250909153,
author = {Pith},
title = {Pith review of: OCELOT 2023: Cell Detection from Cell-Tissue Interaction Challenge},
year = {2026},
howpublished = {\url{https://pith.science/paper/4AQ4OA52}},
note = {Machine review of arXiv:2509.09153}
}
read the original abstract
Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular details to form comprehensive diagnoses. However, existing deep learning-based cell detection models struggle to replicate these behaviors and learn the interdependent semantics between structures at different magnifications. A key barrier in the field is the lack of datasets with multi-scale overlapping cell and tissue annotations. The OCELOT 2023 challenge was initiated to gather insights from the community to validate the hypothesis that understanding cell and tissue (cell-tissue) interactions is crucial for achieving human-level performance, and to accelerate the research in this field. The challenge dataset includes overlapping cell detection and tissue segmentation annotations from six organs, comprising 673 pairs sourced from 306 The Cancer Genome Atlas (TCGA) Whole-Slide Images with hematoxylin and eosin staining, divided into training, validation, and test subsets. Participants presented models that significantly enhanced the understanding of cell-tissue relationships. Top entries achieved up to a 7.99 increase in F1-score on the test set compared to the baseline cell-only model that did not incorporate cell-tissue relationships. This is a substantial improvement in performance over traditional cell-only detection methods, demonstrating the need for incorporating multi-scale semantics into the models. This paper provides a comparative analysis of the methods used by participants, highlighting innovative strategies implemented in the OCELOT 2023 challenge.
Figures
Figures from the paper (2 more)
Reference graph
Works this paper leans on
-
[1]
and Pereira, S
Ahmadi, S.-A. and Pereira, S. (2023). Graphs in biomedical image analysis, and overlapped cell on tissue dataset for histopathology: 5th miccai workshop, grail 2023 and 1st miccai challenge, ocelot 2023, held in conjunction with miccai 2023, vancouver, bc, canada, september 23, and october 4, 2023, proceedings
2023
-
[2]
Amgad, M., Salgado, R., and Cooper, L. A. (2023). A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the mutils model and panoptils dataset. medRxiv
2023
-
[3]
Bai, T., Xu, J., and Xing, F. (2020). Multi-field of view aggregation and context encoding for single-stage nucleus recognition. In Medical Image Computing and Computer Assisted Intervention (MICCAI) , pages 382--392. Springer
2020
-
[4]
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., and Litjens, G. (2020). Automated deep-learning system for gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology , 21(2):233--241
2020
-
[5]
Chao, P., Kao, C.-Y., Ruan, Y.-S., Huang, C.-H., and Lin, Y.-L. (2019). Hardnet: A low memory traffic network. In Proceedings of the IEEE/CVF international conference on computer vision (CVPR) , pages 3552--3561
2019
-
[6]
Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587
arXiv 2017
-
[7]
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV) , pages 801--818
2018
-
[8]
I., Jung, W., Lee, T., Choi, S
Choi, S., Cho, S. I., Jung, W., Lee, T., Choi, S. J., Song, S., Park, G., Park, S., Ma, M., Pereira, S., et al. (2023). Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer. NPJ Breast Cancer , 9(1):71
2023
Show all 51 references
-
[9]
S., Sakellaropoulos, T., Narula, N., Snuderl, M., Feny \"o , D., Moreira, A
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Feny \"o , D., Moreira, A. L., Razavian, N., and Tsirigos, A. (2018). Classification and mutation prediction from non--small cell lung cancer histopathology images using deep learning. Nature medicine , ...
2018
-
[10]
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929
2020 arXiv
-
[11]
D., Byun, J., Obara, B., and Manjunath, B
Gelasca, E. D., Byun, J., Obara, B., and Manjunath, B. (2008). Evaluation and benchmark for biological image segmentation. In 2008 15th IEEE international conference on image processing , pages 1816--1819. IEEE
2008
-
[12]
Gong, Z., Zhang, J., and Guo, W. (2020). Tumor purity as a prognosis and immunotherapy relevant feature in gastric cancer. Cancer medicine , 9(23):9052--9063
2020
-
[13]
Guo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M.-M., and Hu, S.-M. (2023). Visual attention network. Computational Visual Media , 9(4):733--752
2023
-
[14]
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , pages 770--778
2016
-
[15]
o rst, F., Rempe, M., Heine, L., Seibold, C., Keyl, J., Baldini, G., Ugurel, S., Siveke, J., Gr \
H \"o rst, F., Rempe, M., Heine, L., Seibold, C., Keyl, J., Baldini, G., Ugurel, S., Siveke, J., Gr \"u nwald, B., Egger, J., et al. (2024). Cellvit: Vision transformers for precise cell segmentation and classification. Medical Image Analysis , 94:103143
2024
-
[16]
J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). Lora: Low-rank adaptation of large language models. International Conference on Learning Representations (ICLR)
2021
-
[17]
and Zenklusen, J
Hutter, C. and Zenklusen, J. C. (2018). The cancer genome atlas: creating lasting value beyond its data. Cell , 173(2):283--285
2018
-
[18]
Ji, Z., Hu, B., Kuang, J., and Xu, S. (2023). Enhanced cell detection in histopathological images through the integration of tissue information for the ocelot challenge. Technical report, Bio-totem Pte Ltd
2023
-
[19]
Kapil, A., Spitzm \"u ller, A., Brieu, N., Haneder, S., Shumilov, A., Meier, A., Cecchi, F., Barkell, A., Harder, N., Mittermaier, K., et al. (2024). Her2 quantitative continuous scoring for accurate patient selection in her2 negative trastuzumab deruxtecan treated breast canc...
2024
-
[20]
Kingma, D. P. and Ba, J. (2015). Adam: A method for stochastic optimization. In Bengio, Y. and LeCun, Y., editors, 3rd International Conference on Learning Representations ICLR
2015
-
[21]
C., Lo, W.-Y., et al
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 4015--4026
2023
-
[22]
and Ishikawa, S
Komura, D. and Ishikawa, S. (2018). Machine learning methods for histopathological image analysis. Computational and structural biotechnology journal , 16:34--42
2018
-
[23]
A., Jahanifar, M., Tajadin, N
Koohbanani, N. A., Jahanifar, M., Tajadin, N. Z., and Rajpoot, N. (2020). Nuclick: a deep learning framework for interactive segmentation of microscopic images. Medical Image Analysis , 65:101771
2020
-
[24]
M., ElGabry, E
Lara, H., Li, Z., Abels, E., Aeffner, F., Bui, M. M., ElGabry, E. A., Kozlowski, C., Montalto, M. C., Parwani, A. V., Zarella, M. D., et al. (2021). Quantitative image analysis for tissue biomarker use: a white paper from the digital pathology association. Applied Immunohistoc...
2021
-
[25]
Li, Z., Li, W., Mai, H., Zhang, T., and Xiong, Z. (2024). Enhancing cell detection in histopathology images: A vit-based u-net approach. pages 150--160, Cham. Springer, Springer Nature Switzerland
2024
-
[26]
L., Kapur, J., and Weller, D
Liang, H., Naik, A., Williams, C. L., Kapur, J., and Weller, D. S. (2019). Enhanced center coding for cell detection with convolutional neural networks. arXiv preprint arXiv:1904.08864
2019 arXiv
-
[27]
and Yang, C.-H
Lo, Y.-W. and Yang, C.-H. (2024). Enhancing cell detection via fc-hardnet and tissue segmentation: Ocelot 2023 challenge approach. In Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology , pages 130--137, Cham. Springer, Springer Nature...
2024
-
[28]
L., Yoshida, E
Ma, Z., Shiao, S. L., Yoshida, E. J., Swartwood, S., Huang, F., Doche, M. E., Chung, A. P., Knudsen, B. S., and Gertych, A. (2017). Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate. Diagnostic pathol...
2017
-
[29]
S., Borland, D., Woosley, J
Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., and Thomas, N. E. (2009). A method for normalizing histology slides for quantitative analysis. In International Symposium on biomedical imaging (ISBI) , pages 1107--1110. IEEE
2009
-
[30]
L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., M \"u ller, H., Onogur, S., et al
Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A. L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., M \"u ller, H., Onogur, S., et al. (2020). Bias: Transparent reporting of biomedical image analysis challenges. Medical image analysis , 66:101796
2020
-
[31]
Milletari, F., Navab, N., and Ahmadi, S.-A. (2016). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) , pages 565--571. Ieee
2016
-
[32]
Millward, J., He, Z., and Nibali, A. (2024). Dense prediction of cell centroids using tissue context and cell refinement. In Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology , pages 138--149, Cham. Springer, Springer Nature Switzerland
2024
-
[33]
A., Barnholtz-Sloan, J
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Vel \'a zquez Vega, J. E., Brat, D. J., and Cooper, L. A. (2018). Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Science...
2018
-
[34]
J., et al
Park, S., Ock, C.-Y., Kim, H., Pereira, S., Park, S., Ma, M., Choi, S., Kim, S., Shin, S., Aum, B. J., et al. (2022). Artificial intelligence--powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non--small-c...
2022
-
[35]
M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D., et al
Pati, P., Jaume, G., Foncubierta-Rodriguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D., et al. (2022). Hierarchical graph representations in digital pathology. Medical image analysis , 75:102264
2022
-
[36]
M., Shi, W., Alessi, J
Rakaee, M., Adib, E., Ricciuti, B., Sholl, L. M., Shi, W., Alessi, J. V., Cortellini, A., Fulgenzi, C. A., Viola, P., Pinato, D. J., et al. (2023). Association of machine learning--based assessment of tumor-infiltrating lymphocytes on standard histologic images with outcomes o...
2023
-
[37]
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention (MICCAI) , pages 234--241. Springer
2015
-
[38]
V., Shin, J., Park, S., Brattoli, B., Lee, J., Jung, W., Cho, S
Ryu, J., Puche, A. V., Shin, J., Park, S., Brattoli, B., Lee, J., Jung, W., Cho, S. I., Paeng, K., Ock, C.-Y., Yoo, D., and Pereira, S. (2023). Ocelot: Overlapped cell on tissue dataset for histopathology. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patter...
2023
-
[39]
J., Rubin, M
Schaumberg, A. J., Rubin, M. A., and Fuchs, T. J. (2016). H&e-stained whole slide image deep learning predicts spop mutation state in prostate cancer. BioRxiv , page 064279
2016
-
[40]
A., Chatzipli, A., Sirinukunwattana, K., Richman, S., Blake, A., Robineau, J., Mertz, K
Schoenpflug, L. A., Chatzipli, A., Sirinukunwattana, K., Richman, S., Blake, A., Robineau, J., Mertz, K. D., Verrill, C., Leedham, S. J., Hardy, C., et al. (2025). Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis. The Journal of...
2025
-
[41]
Schoenpflug, L. A. and Koelzer, V. H. (2024). Softctm: Cell detection by soft instance segmentation and consideration of cell-tissue interaction. In Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology , pages 109--122, Cham. Springer, ...
2024
-
[42]
Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R., Cree, I. A., and Rajpoot, N. M. (2016). Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE transactions on medical imaging , 35(5):1196--1206
2016
-
[43]
S., Liest l, K., Maddison, J., Askautrud, H
Skrede, O.-J., De Raedt, S., Kleppe, A., Hveem, T. S., Liest l, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., et al. (2020). Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. The Lancet , 395(10221):350--360
2020
-
[44]
Swiderska-Chadaj, Z., Pinckaers, H., van Rijthoven, M., Balkenhol, M., Melnikova, M., Geessink, O., Manson, Q., Sherman, M., Polonia, A., Parry, J., et al. (2019). Learning to detect lymphocytes in immunohistochemistry with deep learning. Medical Image Analysis , 58:101547
2019
-
[45]
Van Rijthoven, M., Balkenhol, M., Sili n a, K., Van Der Laak, J., and Ciompi, F. (2021). Hooknet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. Medical image analysis , 68:101890
2021
-
[46]
N., Kaiser, ., and Polosukhin, I
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, ., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems , 30
2017
-
[47]
D., Ramachandran, R., and Chen, D
Wang, J., MacKenzie, J. D., Ramachandran, R., and Chen, D. Z. (2016). A deep learning approach for semantic segmentation in histology tissue images. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2016: 19th International Conference, Athens, Greece, Octob...
2016
-
[48]
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018). Unified perceptual parsing for scene understanding. In Proceedings of the European conference on computer vision (ECCV) , pages 418--434
2018
-
[49]
M., and Luo, P
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., and Luo, P. (2021). Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in neural information processing systems , 34:12077--12090
2021
-
[50]
Yuan, H., Kido, T., Hirata, M., Ueno, K., Imai, Y., Chen, K., Ren, W., Yang, L., Chen, K., Qu, L., et al. (2024). New vision of hookefficientnet deep neural network: intelligent histopathological recognition system of non-small cell lung cancer. Computers in Biology and Medici...
2024
-
[51]
Zhou, Y., Dou, Q., Chen, H., Qin, J., and Heng, P.-A. (2018). Sfcn-opi: Detection and fine-grained classification of nuclei using sibling fcn with objectness prior interaction. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 32
2018
Reviewed August 4, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.