Hi José,
We will not release human-expert annotations for the remaining cases. This is intentional, because it is practically infeasible to annotate all lesions at the scale of the Private Training Dataset. Hence, we encourage participants to develop methods that can account for or figure out how to use non-annotated cases in the Public Training and Development Dataset as well. At RUMC, we deal with non-annotated training cases with a semi-supervised learning strategy (Bosma et al., 2022). We have released AI-derived csPCa lesion annotations for all 1500 cases in the Public Training and Development Dataset (picai_labels/csPCa_lesion_delineations/AI), using this method. You can choose to use these AI-derived annotations for non-annotated training cases or use your own methodology for the same. Please see https://pi-cai.grand-challenge.org/DATA/ for more details.
For the Private Training Dataset, we will also provide these AI-derived annotations.
More information on how the annotations were derived can be found in our Study Design, Item 23: Annotation characteristics. If you have further questions regarding the annotations or another aspect of the challenge, please let us know.
Hope this helps,
Joeran