  Efficient and precise quantification of lymphocytes in histopathology slides
is imperative for the characterization of the tumor microenvironment and
immunotherapy response insights. We developed a data-centric optimization
pipeline that attain great lymphocyte detection performance using an
off-the-shelf YOLOv5 model, without any architectural modifications. Our
contribution that rely on strategic dataset augmentation strategies, includes
novel biological upsampling and custom visual cohesion transformations tailored
to the unique properties of tissue imagery, and enables to dramatically improve
model performances. Our optimization reveals a pivotal realization: given
intensive customization, standard computational pathology models can achieve
high-capability biomarker development, without increasing the architectural
complexity. We showcase the interest of this approach in the context of breast
cancer where our strategies lead to good lymphocyte detection performances,
echoing a broadly impactful paradigm shift. Furthermore, our data curation
techniques enable crucial histological analysis benchmarks, highlighting
improved generalizable potential.
