3,503 research outputs found
Learning to Segment Breast Biopsy Whole Slide Images
We trained and applied an encoder-decoder model to semantically segment
breast biopsy images into biologically meaningful tissue labels. Since
conventional encoder-decoder networks cannot be applied directly on large
biopsy images and the different sized structures in biopsies present novel
challenges, we propose four modifications: (1) an input-aware encoding block to
compensate for information loss, (2) a new dense connection pattern between
encoder and decoder, (3) dense and sparse decoders to combine multi-level
features, (4) a multi-resolution network that fuses the results of
encoder-decoders run on different resolutions. Our model outperforms a
feature-based approach and conventional encoder-decoders from the literature.
We use semantic segmentations produced with our model in an automated diagnosis
task and obtain higher accuracies than a baseline approach that employs an SVM
for feature-based segmentation, both using the same segmentation-based
diagnostic features.Comment: Added more WSI images in appendi
LEARNet Dynamic Imaging Network for Micro Expression Recognition
Unlike prevalent facial expressions, micro expressions have subtle,
involuntary muscle movements which are short-lived in nature. These minute
muscle movements reflect true emotions of a person. Due to the short duration
and low intensity, these micro-expressions are very difficult to perceive and
interpret correctly. In this paper, we propose the dynamic representation of
micro-expressions to preserve facial movement information of a video in a
single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to
capture micro-level features of an expression in the facial region. The LEARNet
refines the salient expression features in accretive manner by incorporating
accretion layers (AL) in the network. The response of the AL holds the hybrid
feature maps generated by prior laterally connected convolution layers.
Moreover, LEARNet architecture incorporates the cross decoupled relationship
between convolution layers which helps in preserving the tiny but influential
facial muscle change information. The visual responses of the proposed LEARNet
depict the effectiveness of the system by preserving both high- and micro-level
edge features of facial expression. The effectiveness of the proposed LEARNet
is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC.
The experimental results after investigation show a significant improvement of
4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II,
CAS(ME)^2 and SMIC datasets respectively.Comment: Dynamic imaging, accretion, lateral, micro expression recognitio
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