@conference {cJimenezb,
	title = {Segmenting Invasive and In Situ Carcinoma in Breast WSIs with a Pretrained  Histopathology Transformer},
	booktitle = {2nd Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care - MICCAI2025},
	year = {2025},
	month = {09/2025},
	publisher = {Springer LNCS vol 16142},
	organization = {Springer LNCS vol 16142},
	address = {Daejeon, South Korea},
	abstract = {<p>Accurately distinguishing between invasive carcinoma (IC) and carcinoma in situ (CIS) in breast histopathology is essential for diagnosis and staging, and can inform treatment planning. As digital pathology workflows increasingly integrate AI-based tools, automating this distinction in whole slide images (WSIs) remains a critical challenge due to the subtle morphological differences and the limited availability of detailed annotations.</p>},
	keywords = {breast tumor segmentation, carcinoma in situ, foundation model, invasive carcinoma, transformers},
	isbn = {978-3-032-05558-3},
	doi = {10.1007/978-3-032-05559-0_17},
	author = {Lauren Jimenez and Felip Vilardel and Ana Petit and Eduard Dorca and Teresa Soler-Mons{\'o} and Irma Ramos-Oliver and Laura Pons and Pedro L. Fern{\'a}ndez and Laia Adalid-Llansa and Daniel Mata and Lourdes Salazar-Huayna and Josep R Casas and Ver{\'o}nica Vilaplana}
}
