A list of digital pathology image analysis challenges

Adrien Foucart, PhD in biomedical engineering.

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There are several lists of digital pathology challenges and/or datasets floating around in different publications and, of course, on grand-challenge.org, but they never fit exactly what I'm looking for, and either miss some or include some that I would consider as slightly different modalities (such as cytology).

So here is the list I'm including in my thesis, as it may be useful to someone else. I included all challenges between 2010 and 2021 that I could find that used Whole Slide Images (WSIs) and/or image patches extracted from WSIs, either with H&E or IHC staining. I report here the reference to either the post-challenge publication if it exists, or the challenge website if it doesn't, and a (very short) description of the challenge's task(s).

Name, Year Post-challenge publication or website Task(s)
PR in HIMA, 2010 Gurcan, 2010 [1] Lymphocyte segmentation , centroblast detection.
MITOS, 2012 Roux, 2013 [2] Mitosis detection.
AMIDA, 2013 Veta, 2015 [3] Mitosis detection.
MITOS-ATYPIA, 2014 Challenge website Mitosis detection , nuclear atypia scoring.
Brain Tumour DP Challenge, 2014 Challenge website Necrosis region segmentation , gliobastoma multiforme / low grade glioma classification.
Segmentation of Nuclei (SNI) in DP Images, 2015 Description in TCIA wiki Nuclei segmentation.
BIOIMAGING, 2015 Challenge website Tumour classification.
GlaS, 2015 Sirinukunwattana, 2017 [4] Gland segmentation.
TUPAC, 2016 Veta, 2019 [5] Mitotic scoring , PAM50 scoring , mitosis detection.
CAMELYON, 2016 Ehteshami Bejnordi, 2017 [6] Metastases detection.
SNI, 2016 Challenge website Nuclei segmentation.
HER2, 2016 Qaiser, 2018 [7] HER2 scoring.
Tissue MicroarrayAnalysis in ThyroidCancer Diagnosis, 2017 Wang, 2018 [8] Prediction of BRAF gene mutation (classification), TNM stage (scoring), extension status (scoring), tumour size (regression), metastasis status (scoring).
CAMELYON, 2017 Bandi, 2019 [9] Tumour scoring (pN-stage) in lymph nodes.
SNI, 2017 Vu, 2019 [10] Nuclei segmentation.
SNI, 2018 Kurc, 2020 [11] Nuclei segmentation.
ICIAR BACH, 2018 Aresta, 2019 [12] Tumour type patch classification , tumour type segmentation.
MoNuSeg, 2018 Kumar, 2020 [13] Nuclei segmentation.
C-NMC, 2019 Gupta, 2019 [14] Normal/Malignant cell classification.
BreastPathQ, 2019 Petrick, 2021 [15] Tumour cellularity assessment (regression).
PatchCamelyon, 2019 Challenge website Metastasis patch classification.
ACDC@LungHP, 2019 Li, 2019 [16] Lung carcinoma segmentation.
LYON, 2019 Swiderska-Chadaj, 2019 [17] Lymphocyte detection.
PAIP, 2019 Kim, 2021 [18] Tumour segmentation , viable tumour ratio estimation (regression).
Gleason, 2019 Challenge website Tumour scoring , Gleason pattern region segmentation.
DigestPath, 2019 Zhu, 2021 [19] Signet ring cell detection , lesion segmentation , benign/malign tissue classification.
LYSTO, 2019 Challenge website Lymphocyte counting.
BCSS, 2019 Amgad, 2019 [20] Breast cancer regions semantic segmentation.
ANHIR, 2019 Borovec, 2020 [21] WSI registration.
HeroHE, 2020 Conde-Sousa, 2021 [22] HER2 scoring.
MoNuSAC, 2020 Verma, 2021 [23] Nuclei detection , segmentation, and classification.
PANDA, 2020 Bulten, 2022 [24] Prostate cancer Gleason scoring.
PAIP, 2020 Challenge website Colorectal cancer MSI scoring and whole tumour area segmentation.
Seg-PC, 2021 Challenge website Multiple myeloma plasma cells segmentation.
PAIP, 2021 Challenge website Perineural invasion detection and segmentation.
NuCLS, 2021 Amgad, 2021 [25] Nuclei detection , segmentation and classification.
WSSS4LUAD, 2021 Challenge website Tissue semantic segmentation from weak, image-level annotations.
MIDOG, 2021 Challenge website Mitosis detection.

[1] M. N. Gurcan, A. Madabhushi, and N. Rajpoot, "Pattern Recognition in Histopathological Images: An ICPR 2010 Contest," in Lecture Notes in Computer Science, vol. 6388, 2010, pp. 226–234.

[2] L. Roux et al., "Mitosis detection in breast cancer histological images An ICPR 2012 contest," J. Pathol. Inform., vol. 4, no. 1, p. 8, 2013, doi: 10.4103/2153-3539.112693.

[3] M. Veta et al., "Assessment of algorithms for mitosis detection in breast cancer histopathology images," Med. Image Anal., vol. 20, no. 1, pp. 237–248, Feb. 2015, doi: 10.1016/j.media.2014.11.010.

[4] K. Sirinukunwattana, J. P. W. Pluim, H. Chen, and Others, "Gland segmentation in colon histology images: The glas challenge contest," Med. Image Anal., vol. 35, pp. 489–502, 2017, doi: 10.1016/j.media.2016.08.008.

[5] M. Veta et al., "Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge," Med. Image Anal., vol. 54, pp. 111–121, May 2019, doi: 10.1016/j.media.2019.02.012.

[6] B. Ehteshami Bejnordi et al., "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer," JAMA, vol. 318, no. 22, p. 2199, Dec. 2017, doi: 10.1001/jama.2017.14585.

[7] T. Qaiser et al., "HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues," Histopathology, vol. 72, no. 2, pp. 227–238, Jan. 2018, doi: 10.1111/his.13333.

[8] C.-W. Wang et al., "A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays," Bioinformatics, vol. 34, no. 10, pp. 1767–1773, May 2018, doi: 10.1093/bioinformatics/btx838.

[9] P. Bandi et al., "From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge," IEEE Trans. Med. Imaging, vol. 38, no. 2, pp. 550–560, Feb. 2019, doi: 10.1109/TMI.2018.2867350.

[10] Q. D. Vu et al., "Methods for Segmentation and Classification of Digital Microscopy Tissue Images," Front. Bioeng. Biotechnol., vol. 7, Apr. 2019, doi: 10.3389/fbioe.2019.00053.

[11] T. Kurc et al., "Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches," Front. Neurosci., vol. 14, Feb. 2020, doi: 10.3389/fnins.2020.00027.

[12] G. Aresta et al., "BACH: Grand challenge on breast cancer histology images," Med. Image Anal., vol. 56, pp. 122–139, 2019, doi: 10.1016/j.media.2019.05.010.

[13] N. Kumar et al., "A Multi-Organ Nucleus Segmentation Challenge," IEEE Trans. Med. Imaging, vol. 39, no. 5, pp. 1380–1391, 2020, doi: 10.1109/TMI.2019.2947628.

[14] A. Gupta and R. Gupta, Eds., ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Singapore: Springer Singapore, 2019.

[15] N. Petrick et al., "SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment," J. Med. Imaging, vol. 8, no. 03, May 2021, doi: 10.1117/1.JMI.8.3.034501.

[16] Z. Li et al., "Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images - The ACDC@LungHP Challenge 2019," IEEE J. Biomed. Heal. Informatics, vol. 25, no. 2, pp. 429–440, 2021, doi: 10.1109/JBHI.2020.3039741.

[17] Z. Swiderska-Chadaj et al., "Learning to detect lymphocytes in immunohistochemistry with deep learning," Med. Image Anal., vol. 58, p. 101547, Dec. 2019, doi: 10.1016/j.media.2019.101547.

[18] Y. J. Kim et al., "PAIP 2019: Liver cancer segmentation challenge," Med. Image Anal., vol. 67, p. 101854, 2021, doi: 10.1016/j.media.2020.101854.

[19] C. Zhu et al., "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet," Neurocomputing, vol. 438, pp. 165–183, May 2021, doi: 10.1016/j.neucom.2020.04.154.

[20] M. Amgad et al., "Structured crowdsourcing enables convolutional segmentation of histology images," Bioinformatics, vol. 35, no. 18, pp. 3461–3467, 2019, doi: 10.1093/bioinformatics/btz083.

[21] J. Borovec et al., "ANHIR: Automatic Non-Rigid Histological Image Registration Challenge," IEEE Trans. Med. Imaging, vol. 39, no. 10, pp. 3042–3052, Oct. 2020, doi: 10.1109/TMI.2020.2986331.

[22] E. Conde-Sousa et al., "HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization," Nov. 2021.

[23] R. Verma et al., "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge," IEEE Trans. Med. Imaging, vol. 40, no. 12, pp. 3413–3423, Dec. 2021, doi: 10.1109/TMI.2021.3085712.

[24] W. Bulten et al., "Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge," Nat. Med., vol. 28, no. 1, pp. 154–163, Jan. 2022, doi: 10.1038/s41591-021-01620-2.

[25] M. Amgad et al., "NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation," no. Cche 57357, pp. 1–45, 2021.