Computer Vision News - March 2022

57 Tumor InfiltratinG lymphocytes in breast cancER negative results have the worst prognosis. ” This challenge focuses on a specific target: tumor- infiltrating lymphocytes (TILs) . Lymphocytes are immune system cells able to kill tumor cells under certain circumstances. Patients with a higher concentration of lymphocytes in the tumor region seen on histopathology slides tend to have a better prognosis. The way this is determined currently is by pathologists looking at these cells and coming up with a personal and subjective assessment . However, different pathologists will come up with different results. Even the same pathologist can make different assessments of the same case over time, so TILs assessments are not used routinely in clinics . There are twomain goals of this challenge. One is to develop AI models to automate TILs assessments . The second is to validate this assessment on an extensive test dataset . Ultimately, the aim is to make TILs assessments more objective and reproducible to enable their use more confidently in clinical practice. The training dataset has been released in two formats – one is oriented towards the computational pathology community, while one is more compatible with the type of work that computer vision people do. “ We’ve released all the detections in COCO format , ” Mart tells us. “ The COCO format is widely used in many benchmarks to check if detection models work better than other models. Also, we normally train with huge whole-slide images , often 100,000 by 100,000 pixels, but on ImageNet and other databases, you generally see smaller images. We have cropped the regions out of these whole-slide images and cropped the masks so that people don’t have to deal with these big annotations. ” The team created specific libraries (e.g., the WholeSlideData Python package) to make it easy Francesco Ciompi Leslie Tessier Mart van Rijthoven Witali Aswolinskiy