4,903 research outputs found
Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images
In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed
using non-contrast CT (NCCT) for severity assessment. Accurate automated
segmentation of ICH lesions is the initial and essential step, immensely useful
for such assessment. However, compared to other structural imaging modalities
such as MRI, in NCCT images ICH appears with very low contrast and poor SNR.
Over recent years, deep learning (DL)-based methods have shown great potential,
however, training them requires a huge amount of manually annotated
lesion-level labels, with sufficient diversity to capture the characteristics
of ICH. In this work, we propose a novel weakly supervised DL method for ICH
segmentation on NCCT scans, using image-level binary classification labels,
which are less time-consuming and labor-efficient when compared to the manual
labeling of individual ICH lesions. Our method initially determines the
approximate location of ICH using class activation maps from a classification
network, which is trained to learn dependencies across contiguous slices. We
further refine the ICH segmentation using pseudo-ICH masks obtained in an
unsupervised manner. The method is flexible and uses a computationally light
architecture during testing. On evaluating our method on the validation data of
the MICCAI 2022 INSTANCE challenge, our method achieves a Dice value of 0.55,
comparable with those of existing weakly supervised method (Dice value of
0.47), despite training on a much smaller training data
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
The main obstacle to weakly supervised semantic image segmentation is the
difficulty of obtaining pixel-level information from coarse image-level
annotations. Most methods based on image-level annotations use localization
maps obtained from the classifier, but these only focus on the small
discriminative parts of objects and do not capture precise boundaries.
FickleNet explores diverse combinations of locations on feature maps created by
generic deep neural networks. It selects hidden units randomly and then uses
them to obtain activation scores for image classification. FickleNet implicitly
learns the coherence of each location in the feature maps, resulting in a
localization map which identifies both discriminative and other parts of
objects. The ensemble effects are obtained from a single network by selecting
random hidden unit pairs, which means that a variety of localization maps are
generated from a single image. Our approach does not require any additional
training steps and only adds a simple layer to a standard convolutional neural
network; nevertheless it outperforms recent comparable techniques on the Pascal
VOC 2012 benchmark in both weakly and semi-supervised settings.Comment: To appear in CVPR 201
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