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Relevant Papers

• https://www.biorxiv.org/content/biorxiv/early/2017/05/12/064279.full.pdf
  • Goal: Use deep learning to predict SPOP gene mutations in prostate cancer cells
  • Used an ensemble of ResNet-50 models modified with an additional dropout + fully connected layer.
  • Networks were all pretrained on ImageNet data.
  • Each network in the ensemble was trained on a small draw of slide data (had 157 patient slides in total).
  • Weak networks (weak since they were trained on little data) were combined to form a strong learner ensemble
  • Caveat: Paper doesn't provide indication of how well/poorly trained pathologists perform on this data
• A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue (http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0192726&type=printable)
  • Goal: Identify patients with clinical heart failure using H&E tissue
  • Dataset: 209 patients
    • 94 with heart failure
    • 115 w/o failure
  • Compared CNN to traditional feature-engineering model
  • Pathologists classify correctly with 75% accuracy - CNNs beat this with 20% increase on sensitivity and specificity
  • Architecture was modified from that of Janowczyk and Madabhushi (these guys do lots of DL pathology work)
• Classification of breast cancer histology images using transfer learning (https://arxiv.org/pdf/1802.09424.pdf)
  • Investigated the transfer learning performance of InceptionV3 and ResNet-50 with H&E images
    • Specifically studied a breast cancer cell classification task
  • Found that ResNet's performance was better than that of Inception