14,825 research outputs found
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
We introduce a new loss function for the weakly-supervised training of
semantic image segmentation models based on three guiding principles: to seed
with weak localization cues, to expand objects based on the information about
which classes can occur in an image, and to constrain the segmentations to
coincide with object boundaries. We show experimentally that training a deep
convolutional neural network using the proposed loss function leads to
substantially better segmentations than previous state-of-the-art methods on
the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the
working mechanism of our method by a detailed experimental study that
illustrates how the segmentation quality is affected by each term of the
proposed loss function as well as their combinations.Comment: ECCV 201
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
We propose Boundary-RL, a novel weakly supervised segmentation method that
utilises only patch-level labels for training. We envision the segmentation as
a boundary detection problem, rather than a pixel-level classification as in
previous works. This outlook on segmentation may allow for boundary delineation
under challenging scenarios such as where noise artefacts may be present within
the region-of-interest (ROI) boundaries, where traditional pixel-level
classification-based weakly supervised methods may not be able to effectively
segment the ROI. Particularly of interest, ultrasound images, where intensity
values represent acoustic impedance differences between boundaries, may also
benefit from the boundary delineation approach. Our method uses reinforcement
learning to train a controller function to localise boundaries of ROIs using a
reward derived from a pre-trained boundary-presence classifier. The classifier
indicates when an object boundary is encountered within a patch, as the
controller modifies the patch location in a sequential Markov decision process.
The classifier itself is trained using only binary patch-level labels of object
presence, which are the only labels used during training of the entire boundary
delineation framework, and serves as a weak signal to inform the boundary
delineation. The use of a controller function ensures that a sliding window
over the entire image is not necessary. It also prevents possible
false-positive or -negative cases by minimising number of patches passed to the
boundary-presence classifier. We evaluate our proposed approach for a
clinically relevant task of prostate gland segmentation on trans-rectal
ultrasound images. We show improved performance compared to other tested weakly
supervised methods, using the same labels e.g., multiple instance learning.Comment: Accepted to MICCAI Workshop MLMI 2023 (14th International Conference
on Machine Learning in Medical Imaging
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.Comment: 14 pages, 7 figure
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