Latent Diffusion Models with Image-Derived Annotations for Enhanced
  AI-Assisted Cancer Diagnosis in Histopathology
Pedro Osorio Guillermo Jimenez-Perez Javier Montalt-Tordera Jens Hooge Guillem Duran-Ballester Shivam Singh Moritz Radbruch Ute Bach Sabrina Schroeder Krystyna Siudak Julia Vienenkoetter Bettina Lawrenz Sadegh Mohammadi
Artificial Intelligence (AI) based image analysis has an immense potential to
support diagnostic histopathology, including cancer diagnostics. However,
developing supervised AI methods requires large-scale annotated datasets. A
potentially powerful solution is to augment training data with synthetic data.
Latent diffusion models, which can generate high-quality, diverse synthetic
images, are promising. However, the most common implementations rely on
detailed textual descriptions, which are not generally available in this
domain. This work proposes a method that constructs structured textual prompts
from automatically extracted image features. We experiment with the PCam
dataset, composed of tissue patches only loosely annotated as healthy or
cancerous. We show that including image-derived features in the prompt, as
opposed to only healthy and cancerous labels, improves the Fr\'echet Inception
Distance (FID) from 178.8 to 90.2. We also show that pathologists find it
challenging to detect synthetic images, with a median sensitivity/specificity
of 0.55/0.55. Finally, we show that synthetic data effectively trains AI
models.