ArXiv Preprint
The integration of Artificial Intelligence (AI) and Digital Pathology has
been increasing over the past years. Nowadays, applications of deep learning
(DL) methods to diagnose cancer from whole-slide images (WSI) are, more than
ever, a reality within different research groups. Nonetheless, the development
of these systems was limited by a myriad of constraints regarding the lack of
training samples, the scaling difficulties, the opaqueness of DL methods, and,
more importantly, the lack of clinical validation. As such, we propose a system
designed specifically for the diagnosis of colorectal samples. The construction
of such a system consisted of four stages: (1) a careful data collection and
annotation process, which resulted in one of the largest WSI colorectal samples
datasets; (2) the design of an interpretable mixed-supervision scheme to
leverage the domain knowledge introduced by pathologists through spatial
annotations; (3) the development of an effective sampling approach based on the
expected severeness of each tile, which decreased the computation cost by a
factor of almost 6x; (4) the creation of a prototype that integrates the full
set of features of the model to be evaluated in clinical practice. During these
stages, the proposed method was evaluated in four separate test sets, two of
them are external and completely independent. On the largest of those sets, the
proposed approach achieved an accuracy of 93.44%. DL for colorectal samples is
a few steps closer to stop being research exclusive and to become fully
integrated in clinical practice.
Pedro C. Neto, Diana Montezuma, Sara P. Oliveira, Domingos Oliveira, João Fraga, Ana Monteiro, João Monteiro, Liliana Ribeiro, Sofia Gonçalves, Stefan Reinhard, Inti Zlobec, Isabel M. Pinto, Jaime S. Cardoso
2023-01-06
