68,962 research outputs found
Learning strategies for improving neural networks for image segmentation under class imbalance
This thesis aims to improve convolutional neural networks (CNNs) for image segmentation under class imbalance, which is referred to the problem of training dataset when the class distributions are unequal. We particularly focus on medical image segmentation because of its imbalanced nature and clinical importance.
Based on our observations of model behaviour, we argue that CNNs cannot generalize well on imbalanced segmentation tasks, mainly because of two counterintuitive reasons. CNNs are prone to overfit the under-represented foreground classes as it would memorize the regions of interest (ROIs) in the training data because they are so rare. Besides, CNNs could underfit the heterogenous background classes as it is difficult to learn from the samples with diverse and complex characteristics. Those behaviours of CNNs are not limited to specific loss functions.
To address those limitations, firstly we propose novel asymmetric variants of popular loss functions and regularization techniques, which are explicitly designed to increase the variance of foreground samples to counter overfitting under class imbalance. Secondly we propose context label learning (CoLab) to tackle background underfitting by automatically decomposing the background class into several subclasses. This is achieved by optimizing an auxiliary task generator to generate context labels such that the main network will produce good ROIs segmentation performance. Then we propose a meta-learning based automatic data augmentation framework which builds a balance of foreground and background samples to alleviate class imbalance. Specifically, we learn class-specific training-time data augmentation (TRA) and jointly optimize TRA and test-time data augmentation (TEA) effectively aligning training and test data distribution for better generalization. Finally, we explore how to estimate model performance under domain shifts when trained with imbalanced dataset. We propose class-specific variants of existing confidence-based model evaluation methods which adapts separate parameters per class, enabling class-wise calibration to reduce model bias towards the minority classes.Open Acces
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Semantic segmentation is essentially important to biomedical image analysis.
Many recent works mainly focus on integrating the Fully Convolutional Network
(FCN) architecture with sophisticated convolution implementation and deep
supervision. In this paper, we propose to decompose the single segmentation
task into three subsequent sub-tasks, including (1) pixel-wise image
segmentation, (2) prediction of the class labels of the objects within the
image, and (3) classification of the scene the image belonging to. While these
three sub-tasks are trained to optimize their individual loss functions of
different perceptual levels, we propose to let them interact by the task-task
context ensemble. Moreover, we propose a novel sync-regularization to penalize
the deviation between the outputs of the pixel-wise segmentation and the class
prediction tasks. These effective regularizations help FCN utilize context
information comprehensively and attain accurate semantic segmentation, even
though the number of the images for training may be limited in many biomedical
applications. We have successfully applied our framework to three diverse 2D/3D
medical image datasets, including Robotic Scene Segmentation Challenge 18
(ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus
Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all
three challenges.Comment: IEEE Transactions on Medical Imagin
Visual Chunking: A List Prediction Framework for Region-Based Object Detection
We consider detecting objects in an image by iteratively selecting from a set
of arbitrarily shaped candidate regions. Our generic approach, which we term
visual chunking, reasons about the locations of multiple object instances in an
image while expressively describing object boundaries. We design an
optimization criterion for measuring the performance of a list of such
detections as a natural extension to a common per-instance metric. We present
an efficient algorithm with provable performance for building a high-quality
list of detections from any candidate set of region-based proposals. We also
develop a simple class-specific algorithm to generate a candidate region
instance in near-linear time in the number of low-level superpixels that
outperforms other region generating methods. In order to make predictions on
novel images at testing time without access to ground truth, we develop
learning approaches to emulate these algorithms' behaviors. We demonstrate that
our new approach outperforms sophisticated baselines on benchmark datasets.Comment: to appear at ICRA 201
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