1,010 research outputs found
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised CNN
segmentation, which devises a regression network to learn image-level
information such as the size of a target region. These regressions are used to
effectively regularize the segmentation network, constraining softmax
predictions of the unlabeled images to match the inferred label distributions.
Our framework is based on inequality constraints that tolerate uncertainties
with inferred knowledge, e.g., regressed region size, and can be employed for a
large variety of region attributes. We evaluated our proposed strategy for left
ventricle segmentation in magnetic resonance images (MRI), and compared it to
standard proposal-based semi-supervision strategies. Our strategy leverages
unlabeled data in more efficiently, and achieves very competitive results,
approaching the performance of full-supervision.Comment: Accepted as paper as MICCAI 2O1
Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions
This study investigates the optimization aspects of imposing hard inequality
constraints on the outputs of CNNs. In the context of deep networks,
constraints are commonly handled with penalties for their simplicity, and
despite their well-known limitations. Lagrangian-dual optimization has been
largely avoided, except for a few recent works, mainly due to the computational
complexity and stability/convergence issues caused by alternating explicit dual
updates/projections and stochastic optimization. Several studies showed that,
surprisingly for deep CNNs, the theoretical and practical advantages of
Lagrangian optimization over penalties do not materialize in practice. We
propose log-barrier extensions, which approximate Lagrangian optimization of
constrained-CNN problems with a sequence of unconstrained losses. Unlike
standard interior-point and log-barrier methods, our formulation does not need
an initial feasible solution. Furthermore, we provide a new technical result,
which shows that the proposed extensions yield an upper bound on the duality
gap. This generalizes the duality-gap result of standard log-barriers, yielding
sub-optimality certificates for feasible solutions. While sub-optimality is not
guaranteed for non-convex problems, our result shows that log-barrier
extensions are a principled way to approximate Lagrangian optimization for
constrained CNNs via implicit dual variables. We report comprehensive weakly
supervised segmentation experiments, with various constraints, showing that our
formulation outperforms substantially the existing constrained-CNN methods,
both in terms of accuracy, constraint satisfaction and training stability, more
so when dealing with a large number of constraints
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images
The visual examination of tissue biopsy sections is fundamental for cancer
diagnosis, with pathologists analyzing sections at multiple magnifications to
discern tumor cells and their subtypes. However, existing attention-based
multiple instance learning (MIL) models, used for analyzing Whole Slide Images
(WSIs) in cancer diagnostics, often overlook the contextual information of
tumor and neighboring tiles, leading to misclassifications. To address this, we
propose the Context-Aware Multiple Instance Learning (CAMIL) architecture.
CAMIL incorporates neighbor-constrained attention to consider dependencies
among tiles within a WSI and integrates contextual constraints as prior
knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell
lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16) metastasis,
achieving test AUCs of 0.959\% and 0.975\%, respectively, outperforming other
state-of-the-art methods. Additionally, CAMIL enhances model interpretability
by identifying regions of high diagnostic value.Comment: 16 pages, 4 figure
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