  Delineation of cancerous regions in gigapixel whole slide images (WSIs) is a
crucial diagnostic procedure in digital pathology. This process is
time-consuming because of the large search space in the gigapixel WSIs, causing
chances of omission and misinterpretation at indistinct tumor lesions. To
tackle this, the development of an automated cancerous region segmentation
method is imperative. We frame this issue as a modeling problem with partial
label WSIs, where some cancerous regions may be misclassified as benign and
vice versa, producing patches with noisy labels. To learn from these patches,
we propose Self-similarity Student, combining teacher-student model paradigm
with similarity learning. Specifically, for each patch, we first sample its
similar and dissimilar patches according to spatial distance. A teacher-student
model is then introduced, featuring the exponential moving average on both
student model weights and teacher predictions ensemble. While our student model
takes patches, teacher model takes all their corresponding similar and
dissimilar patches for learning robust representation against noisy label
patches. Following this similarity learning, our similarity ensemble merges
similar patches' ensembled predictions as the pseudo-label of a given patch to
counteract its noisy label. On the CAMELYON16 dataset, our method substantially
outperforms state-of-the-art noise-aware learning methods by 5$\%$ and the
supervised-trained baseline by 10$\%$ in various degrees of noise. Moreover,
our method is superior to the baseline on our TVGH TURP dataset with 2$\%$
improvement, demonstrating the generalizability to more clinical histopathology
segmentation tasks.
