The Early Bird deadline has closed, thank you for your early submissions! The final challenge deadline is the 31st of August.
Tasks Overview¶
The UNICORN challenge includes 20 tasks designed to evaluate model performance across both radiology and pathology. Each task has a defined time limit, which sets the maximum allowed runtime for the algorithm Docker container. For vision and vision-language tasks, each case is processed individually, so the time limit applies per case. For language tasks, the algorithm processes all reports in a single run, meaning the time limit applies to the entire dataset.¶
Note: Additional tasks may be introduced in the future, also as additional (hidden) test data.
ID | Task Name | Task Type | Modality | Domain | Metric | Time limit | Task Description |
---|---|---|---|---|---|---|---|
T1 | Classifying HE prostate biopsies into ISUP scores | Classification | Vision | Pathology | quadratic weighted kappa | 600s | View Full Task Details |
T2 | Classifying lung nodule malignancy in CT | Classification | Vision | Radiology | AUC | 300s | View Full Task Details |
T3 | Predicting the time to biochemical recurrence in HE prostatectomies | Regression | Vision | Pathology | censored c-index | 1500s | View Full Task Details |
T4 | Predicting slide-level tumor proportion score in NSCLC IHC-stained WSI | Classification | Vision | Pathology | quadratic weighted kappa | 600s | View Full Task Details |
T5 | Detecting signet ring cells in HE-stained WSI of gastric cancer | Detection | Vision | Pathology | F1 score | 600s | View Full Task Details |
T6 | Detecting clinically significant cancer in prostate MRI exams | Detection | Vision | Radiology | average of AUROC and AP | 600s | View Full Task Details |
T7 | Detecting lung nodules in thoracic CT | Detection | Vision | Radiology | sensitivity | 300s | View Full Task Details |
T8 | Detecting mitotic figures in breast cancer HE-stained WSIs | Detection | Vision | Pathology | F1 score | 600s | View Full Task Details |
T9 | Segmenting ROIs in breast cancer HE-stained WSIs | Segmentation | Vision | Pathology | DICE | 300s | View Full Task Details |
T10 | Segmenting lesions within ROIs in CT | Segmentation | Vision | Radiology | DICE, long- and short-axis errors | 600s | View Full Task Details |
T11 | Segmenting three anatomical structures in lumbar spine MRI | Segmentation | Vision | Radiology | DICE | 600s | View Full Task Details |
T12 | Predicting histopathology sample origin | Classification | Language | Pathology | unweighted kappa | 7200s | View Full Task Details |
T13 | Classifying pulmonary nodule presence | Classification | Language | Radiology | AUC | 7200s | View Full Task Details |
T14 | Classifying kidney abnormality | Classification | Language | Radiology | AUC | 7200s | View Full Task Details |
T15 | Predicting Hip Kellgren-Lawrence score | Classification | Language | Radiology | unweighted kappa | 7200s | View Full Task Details |
T16 | Classifying colon histopathology diagnosis | Classification | Language | Pathology | macro AUC | 7200s | View Full Task Details |
T17 | Predicting lesion size measurements | Regression | Language | Radiology | RSMAPE | 7200s | View Full Task Details |
T18 | Predicting prostate volume, PSA, and PSA density | Regression | Language | Radiology | RSMAPE | 7200s | View Full Task Details |
T19 | Anonymizing report | Named Entity Recognition | Language | Radiology + Pathology | weighted F1 | 7200s | View Full Task Details |
T20 | Generating caption from WSI | Generation | Vision-Language | Pathology | BLEU-4, ROUGE-L, METEOR, CIDER | 1500 s | View Full Task Details |