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cancercomputational pathologycomputer visiondeep learninggrand-challenge.orghistopathologylife sciences
"This dataset contains the all data for the CAncer MEtastases in LYmph nOdes challeNge or CAMELYON. CAMELYON was the first challenge using whole-slide images in computational pathology and aimed to help pathologists identify breast cancer metastases in sentinel lymph nodes. Lymph node metastases are extremely important to find, as they indicate that the cancer is no longer localized and systemic treatment might be warranted. Searching for these metastases in H&E-stained tissue is difficult and time-consuming and AI algorithms can play a role in helping make this faster and more accura...
cancerclassificationcomputational pathologycomputer visiondeep learningdigital pathologygrand-challenge.orghistopathologyimaginglife sciencesmachine learningmedical image computingmedical imaging
This dataset contains the training data for the Machine learning for Optimal detection of iNflammatory cells in the KidnEY or MONKEY challenge. The MONKEY challenge focuses on the automated detection and classification of inflammatory cells, specifically monocytes and lymphocytes, in kidney transplant biopsies using Periodic acid-Schiff (PAS) stained whole-slide images (WSI). It contains 80 WSI, collected from 4 different pathology institutes, with annotated regions of interest. For each WSI up to 3 different PAS scans and one IHC slide scan are available. This dataset and challenge support th...
computed tomographycomputer visioncoronavirusCOVID-19grand-challenge.orgimaginglife sciencesSARS-CoV-2
The STOIC project collected Computed Tomography (CT) images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence, based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This S3 bucket contains the training sample of the STOIC dataset as used in the STOIC2021 challenge on grand-challenge.org.
cancercomputational pathologycomputer visiondeep learninggrand-challenge.orghistopathologylife sciences
"This dataset contains the all data for the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge or LEOPARD.Prostate cancer, impacting 1.4 million men annually, is a prevalent malignancy (H. Sung et al., 2021). A substantial number of these individuals undergo prostatectomy as the primary curative treatment. The efficacy of this surgery is assessed, in part, by monitoring the concentration of prostate-specific antigen (PSA) in the bloodstream. While the role of PSA in prostate cancer screening is debatable (W. F. Clark et al., 2018; E. A. M. Heijnsdijk et al., 2018), it serves as a valuable biomarker for postprostatectomy follow-up in patients. Following successful surgery, PSA concentration is typically undetectable (<0.1 ng/mL) within 4-6 weeks (S. S. Goonewardene et al., 2014). However, approximately 30% of patients experience biochemical recurrence, signifying the resurgence of prostate cancer cells. This recurrence serves as a prognostic indicator for progression to clinical metastases and eventual prostate cancer-related mortality (C. L. Amling, 2014; S. J. Freedland et al., 2005; M. Han et al., 2001; T. Van den Broeck et al., 2001. Current clinical practices gauge the risk of biochemical recurrence by considering the International Society of Urological Pathology (ISUP) grade, PSA value at diagnosis, and TNM staging criteria (J. I. Epstein et al., 2016). A recent European consensus guideline suggests categorizing patients into low-risk, intermediate-risk, and high-risk groups based on these factors (N. Mottet et al., 2021). Notably, a high ISUP grade independently assigns a patient to the intermediate (grade 2/3) or high-risk group (grade 4/5). The Gleason growth patterns, representing morphological patterns of prostate cancer, are used to categorize cancerous tissue into ISUP grade groups (J. I. Epstein, 2010; P. M. Pierorazio et al., 2013; G. J. L. H. van Leenders et al., 2020; J. I. Epstein et al., 2016). However, the ISUP grade has limitations, such as grading disagreement among pathologists (J. I. Epstein et al., 2016) and coarse descriptors of tissue morphology. Recently, deep learning was shown (H. Pinckaers et al., 2022; O. Eminaga et. al., 2024)...
cancercomputational pathologycomputer visiondeep learninggrand-challenge.orghistopathologylife sciences
"This dataset contains the training data for the Tumor InfiltratinG lymphocytes in breast cancER or TIGER challenge. TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in breast cancer histopathology slides. TILs are proving to be an important biomarker in cancer patients as they can play a part in killing tumor cells, particularly in some types of breast cancer. Identifying and measuring TILs can help to better target treatments, particularly immunotherapy, and may result in lower levels of other more aggressive treatments, including chemo...