In recent years, deep neural networks have achieved
state-of-the-art performance in a variety of recognition and
segmentation tasks in medical imaging including brain tumor
segmentation. We investigate that segmenting a brain tumor is
facing to the imbalanced data problem where the number of pixels
belonging to the background class (non tumor pixel) is much
larger than the number of pixels belonging to the foreground
class (tumor pixel). To address this problem, we propose a multitask
network which is formed as a cascaded structure. Our model
consists of two targets, i.e., (i) effectively differentiate the brain
tumor regions and (ii) estimate the brain tumor mask. The first
objective is performed by our proposed contextual brain tumor
detection network, which plays a role of an attention gate and
focuses on the region around brain tumor only while ignoring the
far neighbor background which is less correlated to the tumor.
Different from other existing object detection networks which
process every pixel, our contextual brain tumor detection network
only processes contextual regions around ground-truth instances
and this strategy aims at producing meaningful regions proposals.
The second objective is built upon a 3D atrous residual network
and under an encode-decode network in order to effectively segment
both large and small objects (brain tumor). Our 3D atrous
residual network is designed with a skip connection to enables the
gradient from the deep layers to be directly propagated to shallow
layers, thus, features of different depths are preserved and used
for refining each other. In order to incorporate larger contextual
information from volume MRI data, our network utilizes the
3D atrous convolution with various kernel sizes, which enlarges
the receptive field of filters. Our proposed network has been
evaluated on various datasets including BRATS2015, BRATS2017
and BRATS2018 datasets with both validation set and testing
set. Our performance has been benchmarked by both regionbased
metrics and surface-based metrics. We also have conducted
comparisons against state-of-the-art approaches
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Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability penalizes loss more on foreground pixels while less on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks.
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- Award ID(s):
- 1751402
- NSF-PAR ID:
- 10145451
- Date Published:
- Journal Name:
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Page Range / eLocation ID:
- 6970 to 6980
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCV.2019.00707
