19,332 research outputs found
DynaMask: Dynamic Mask Selection for Instance Segmentation
The representative instance segmentation methods mostly segment different
object instances with a mask of the fixed resolution, e.g., 28*28 grid.
However, a low-resolution mask loses rich details, while a high-resolution mask
incurs quadratic computation overhead. It is a challenging task to predict the
optimal binary mask for each instance. In this paper, we propose to dynamically
select suitable masks for different object proposals. First, a dual-level
Feature Pyramid Network (FPN) with adaptive feature aggregation is developed to
gradually increase the mask grid resolution, ensuring high-quality segmentation
of objects. Specifically, an efficient region-level top-down path (r-FPN) is
introduced to incorporate complementary contextual and detailed information
from different stages of image-level FPN (i-FPN). Then, to alleviate the
increase of computation and memory costs caused by using large masks, we
develop a Mask Switch Module (MSM) with negligible computational cost to select
the most suitable mask resolution for each instance, achieving high efficiency
while maintaining high segmentation accuracy. Without bells and whistles, the
proposed method, namely DynaMask, brings consistent and noticeable performance
improvements over other state-of-the-arts at a moderate computation overhead.
The source code: https://github.com/lslrh/DynaMask.Comment: Accepted by CVPR202
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
The analysis of glandular morphology within colon histopathology images is an
important step in determining the grade of colon cancer. Despite the importance
of this task, manual segmentation is laborious, time-consuming and can suffer
from subjectivity among pathologists. The rise of computational pathology has
led to the development of automated methods for gland segmentation that aim to
overcome the challenges of manual segmentation. However, this task is
non-trivial due to the large variability in glandular appearance and the
difficulty in differentiating between certain glandular and non-glandular
histological structures. Furthermore, a measure of uncertainty is essential for
diagnostic decision making. To address these challenges, we propose a fully
convolutional neural network that counters the loss of information caused by
max-pooling by re-introducing the original image at multiple points within the
network. We also use atrous spatial pyramid pooling with varying dilation rates
for preserving the resolution and multi-level aggregation. To incorporate
uncertainty, we introduce random transformations during test time for an
enhanced segmentation result that simultaneously generates an uncertainty map,
highlighting areas of ambiguity. We show that this map can be used to define a
metric for disregarding predictions with high uncertainty. The proposed network
achieves state-of-the-art performance on the GlaS challenge dataset and on a
second independent colorectal adenocarcinoma dataset. In addition, we perform
gland instance segmentation on whole-slide images from two further datasets to
highlight the generalisability of our method. As an extension, we introduce
MILD-Net+ for simultaneous gland and lumen segmentation, to increase the
diagnostic power of the network.Comment: Initial version published at Medical Imaging with Deep Learning
(MIDL) 201
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