2 research outputs found
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting
Histopathology-based survival modelling has two major hurdles. Firstly, a
well-performing survival model has minimal clinical application if it does not
contribute to the stratification of a cancer patient cohort into different risk
groups, preferably driven by histologic morphologies. In the clinical setting,
individuals are not given specific prognostic predictions, but are rather
predicted to lie within a risk group which has a general survival trend. Thus,
It is imperative that a survival model produces well-stratified risk groups.
Secondly, until now, survival modelling was done in a two-stage approach
(encoding and aggregation). The massive amount of pixels in digitized whole
slide images were never utilized to their fullest extent due to technological
constraints on data processing, forcing decoupled learning. EPIC-Survival
bridges encoding and aggregation into an end-to-end survival modelling
approach, while introducing stratification boosting to encourage the model to
not only optimize ranking, but also to discriminate between risk groups. In
this study we show that EPIC-Survival performs better than other approaches in
modelling intrahepatic cholangiocarcinoma, a historically difficult cancer to
model. Further, we show that stratification boosting improves further improves
model performance, resulting in a concordance-index of 0.880 on a held-out test
set. Finally, we were able to identify specific histologic differences, not
commonly sought out in ICC, between low and high risk groups.Comment: co-first authors: Hassan Muhammad and Chensu Xi
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification
In recent years, the availability of digitized Whole Slide Images (WSIs) has
enabled the use of deep learning-based computer vision techniques for automated
disease diagnosis. However, WSIs present unique computational and algorithmic
challenges. WSIs are gigapixel-sized (100K pixels), making them
infeasible to be used directly for training deep neural networks. Also, often
only slide-level labels are available for training as detailed annotations are
tedious and can be time-consuming for experts. Approaches using
multiple-instance learning (MIL) frameworks have been shown to overcome these
challenges. Current state-of-the-art approaches divide the learning framework
into two decoupled parts: a convolutional neural network (CNN) for encoding the
patches followed by an independent aggregation approach for slide-level
prediction. In this approach, the aggregation step has no bearing on the
representations learned by the CNN encoder. We have proposed an end-to-end
framework that clusters the patches from a WSI into -groups, samples
patches from each group for training, and uses an adaptive attention
mechanism for slide level prediction; Cluster-to-Conquer (C2C). We have
demonstrated that dividing a WSI into clusters can improve the model training
by exposing it to diverse discriminative features extracted from the patches.
We regularized the clustering mechanism by introducing a KL-divergence loss
between the attention weights of patches in a cluster and the uniform
distribution. The framework is optimized end-to-end on slide-level
cross-entropy, patch-level cross-entropy, and KL-divergence loss
(Implementation: https://github.com/YashSharma/C2C).Comment: Accepted at MIDL, 2021 - https://openreview.net/forum?id=7i1-2oKIEL