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Found 13 papers, 12 papers with code
Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images
Our method is composed of two networks: a localizer that yields segmentation mask, followed by a classifier.
Negative Evidence Matters in Interpretable Histology Image Classification
The CNN is exploited to collect both positive and negative evidence at the pixel level to train the decoder.
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling
Interpolation is required to restore full size CAMs, yet it does not consider the statistical properties of objects, such as color and texture, leading to activations with inconsistent boundaries, and inaccurate localizations.
Holistic Guidance for Occluded Person Re-Identification
Hence, our proposed student-teacher framework is trained to address the occlusion problem by matching the distributions of between- and within-class distances (DCDs) of occluded samples with that of holistic (non-occluded) samples, thereby using the latter as a soft labeled reference to learn well separated DCDs.
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations.
Deep Active Learning for Joint Classification & Segmentation with Weak Annotator
CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions.
Convolutional STN for Weakly Supervised Object Localization
Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance.
Non-parametric Uni-modality Constraints for Deep Ordinal Classification
We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequality constraints over all the pairs of adjacent labels.
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Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Comparative Study
Four key challenges are identified for the application of deep WSOL methods in histology -- under/over activation of CAMs, sensitivity to thresholding, and model selection.
Min-max Entropy for Weakly Supervised Pointwise Localization
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.
Neural Networks Regularization Through Representation Learning
In this thesis, we tackle the neural network overfitting issue from a representation learning perspective by considering the situation where few training samples are available which is the case of many real world applications.
Neural Networks Regularization Through Class-wise Invariant Representation Learning
In this work, we tackle the issue of training neural networks for classification task when few training samples are available.
Deep Neural Networks Regularization for Structured Output Prediction
The motivation of this work is to learn the output dependencies that may lie in the output data in order to improve the prediction accuracy.
