Towards More Transparent and Accurate Cancer Diagnosis with an
  Unsupervised CAE Approach
Zahra Tabatabaei Adrian Colomer Javier Oliver Moll Valery Naranjo
Digital pathology has revolutionized cancer diagnosis by leveraging
Content-Based Medical Image Retrieval (CBMIR) for analyzing histopathological
Whole Slide Images (WSIs). CBMIR enables searching for similar content,
enhancing diagnostic reliability and accuracy. In 2020, breast and prostate
cancer constituted 11.7% and 14.1% of cases, respectively, as reported by the
Global Cancer Observatory (GCO). The proposed Unsupervised CBMIR (UCBMIR)
replicates the traditional cancer diagnosis workflow, offering a dependable
method to support pathologists in WSI-based diagnostic conclusions. This
approach alleviates pathologists' workload, potentially enhancing diagnostic
efficiency. To address the challenge of the lack of labeled histopathological
images in CBMIR, a customized unsupervised Convolutional Auto Encoder (CAE) was
developed, extracting 200 features per image for the search engine component.
UCBMIR was evaluated using widely-used numerical techniques in CBMIR, alongside
visual evaluation and comparison with a classifier. The validation involved
three distinct datasets, with an external evaluation demonstrating its
effectiveness. UCBMIR outperformed previous studies, achieving a top 5 recall
of 99% and 80% on BreaKHis and SICAPv2, respectively, using the first
evaluation technique. Precision rates of 91% and 70% were achieved for BreaKHis
and SICAPv2, respectively, using the second evaluation technique. Furthermore,
UCBMIR demonstrated the capability to identify various patterns in patches,
achieving an 81% accuracy in the top 5 when tested on an external image from
Arvaniti.