=Paper=
{{Paper
|id=Vol-2886/paper7
|storemode=property
|title=Joint Polyp Detection and Segmentation with Heterogeneous Endoscopic Data
|pdfUrl=https://ceur-ws.org/Vol-2886/paper7.pdf
|volume=Vol-2886
|authors=Wuyang LI,Chen YANG,Jie LIU, Xinyu LIU, Xiaoqing GUO,Yixuan YUAN
|dblpUrl=https://dblp.org/rec/conf/isbi/LiYLLGY21
}}
==Joint Polyp Detection and Segmentation with Heterogeneous Endoscopic Data==
https://ceur-ws.org/Vol-2886/paper7.pdf
Joint Polyp Detection and Segmentation with
Heterogeneous Endoscopic Data
Wuyang LIa , Chen YANGa , Jie LIUa , Xinyu LIUa , Xiaoqing GUOa and
Yixuan YUANa
a
City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, 999077, Hong Kong SAR, China
Abstract
Endoscopy is commonly used for the early diagnosis of colorectal cancer. However, the endoscope
images are usually obtained under different illumination conditions, at various sites of the digestive
tract, and from multiple medical centers. The collected heterogeneous dataset is a challenging problem
in developing automatic and accurate segmentation and detection models. To address these issues, we
propose comprehensive polyp detection and segmentation in endoscopic scenarios with novel insights
and strategies. For the detection task, we perform joint optimization of classification and regression
with adaptive training sample selection strategies in order to deal with the heterogeneous problem. Our
detection model achieves 1st place in both first and second rounds of EndoCV 2021 polyp detection
challenge. Specifically, the proposed detection framework achieves full-scores (1.0) on APπππππ and
APππππππ in the 1π π‘ round, and 0.8986 Β± 0.1920 of score-d on the 2ππ round. For the segmentation
task, we employ HRNet as our backbone and propose a low-rank module to enhance the generalization
ability across multiple heterogeneous datasets. Our segmentation model achieves 0.7771 Β± 0.0695 score
and ranked 4th place in EndoCV 2021 polyp segmentation challenge.
1. Introduction
Colorectal cancer (CRC) is the second common cause of cancer-related deaths in the United
States, with 53,200 estimated deaths in 2020. Fortunately, if an adenomatous polyp is detected
and removed at its early stage, the deaths caused by CRC can be significantly reduced, and the
survival rate is as high as 90%. Endoscopy is a commonly utilized clinical process to identify
adenomatous polyps [1]. This process is usually performed manually by the clinician, which may
suffer from human error and missed diagnosis of the polyp. Hence, there is a high demand for
automatic polyp detection and segmentation models with satisfactory accuracy to facilitate the
endoscopy procedures. Even though many methods [2, 3, 4, 5, 6] have been built for automatic
detection and segmentation of polyps, existing models are mainly trained with homogeneous
data collected from unique medical centers, and learning with highly heterogeneous dataset
remains an open problem.
Polyp Detection Task. Most of the existing works [3, 2, 7] about polyp detection tend to
perform model ensemble and blindly increase the scale of neural networks for heterogeneous
datasets. However, this will lead to two potential inconsistencies, (1) Optimization Inconsistency
(OI): The inconsistent optimization targets of classification and regression, and (2) Data Incon-
sistency (DI): The inconsistent standards of colonoscopy polyp annotations. To handle these
3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV2021) in conjunction with the
18th IEEE International Symposium on Biomedical Imaging ISBI2021, April 13th, 2021, Nice, France
" Corresponding author: yxyuan.ee@cityu.edu.hk (Y. YUAN)
Β© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
two problems, we utilize GFL v2 [8] to jointly optimize classification and regression and use
the regression offset distribution to relieve the influence of ambiguous annotations. To further
improve the generalization ability of neural networks, we utilize Adaptive Training Sample
Selection (ATSS) [9] strategy to select high-quality anchors with diverse spatial distributions.
Polyp Segmentation Task. Deep convolutional neural networks have achieved impressive
progress in polyp segmentation task [6]. Most of the existing methods utilize existing networks,
such as VGGNet, Unet, and Dilated ResNet, as the feature extractor. These models gradually
reduce the feature resolution through convolution layers and pooling layers and recover the
raw resolution through interpolation and convolution operation. This strategy will lead to
intermediate low-resolution feature representations and lose a lot of critical detailed information,
which is not an optimal solution for polyp semantic segmentation, i.e., pixel-wise classification.
Thus, we employ HRNet [10] as our backbone for polyp segmentation in EndoCV2021 challenge1 .
Furthermore, considering this dataset heterogeneous property, we propose a low-rank module
to enhance the generalization.
2. Proposed Methods
2.1. Method Details for Polyp Detection
As illustrated in Figure 1. Given an image, we first adopt ResNeXt-101-DCN with Feature
Pyramid Network (FPN) [11] for feature extraction (Β§2.1.1). To relieve the aforementioned
inconsistency (Β§2.1.2) , we perform joint optimization of classification and regression to bridge OI
and use the regression offset distribution to relieve DI [8] in detection heads. To further improve
the generalization ability (Β§2.1.3) of detection framework, ATSS [9] strategy is introduced to
select high-quality training samples with diverse spatial distributions.
2.1.1. Feature Extraction
Due to the heterogeneous samples obtained from different medical centers, we found AP
improves with the increase of model scale without over-fitting. Hence, we adopted ResNeXt101-
DCN as our feature extractor. Specifically, we apply deformable convolutions from stage 3 to 5
of ResNeXt-101 and frozen parameters in stage 1. Besides, FPN is adopted in the backbone for
multi-scale feature fusion.
2.1.2. Solutions for the Two Inconsistency
OI and DI are the major limitations for the performance of polyp detection in EndoCV 2021
challenges. For OI, classification and regression are optimized in two separated branches
with inconsistent supervisions, which brings about the inconsistency during performing Non-
Maximum Suppression (NMS) in inference. For DI, we found a large variance of bounding box
coordinates caused by inconsistent standards of manual annotations. As shown in Figure 2
Left, the annotations on two sequential video frames should be similar but are different, obvi-
ously. Therefore, these ambiguous boxes will confuse neural networks and affect the detection
performance significantly, especially in the case of small-scale endoscopic datasets.
1
https://endocv2021.grand-challenge.org/EndoCV2021/
Figure 1: The overview of the detection framework. Specifically, ResNeXt-101-DCN with FPN necks
are used as our feature extractors. Besides, GFL v2 [8] and ATSS [9] are these two key components in
our model for joint optimizing classification and regression with diverse training samples.
Figure 2: Left: Illustration of the phenomenon of DI. Ambiguous annotations may cause a severe per-
formance drop in polyp detection; Right: The biased scale distribution of EndoCV 2021 polyp detection
dataset. The area of most polyp instances is larger than size 96Γ96 pixels, which is defined as a large
object in MS COCO matrix.
Most existing works tend to relieve OI by introducing localization quality estimation strategies,
which are performed in the regression branch for comprehensive representations of detection
results, such as the centerness in FCOS [12] and the Intersection of Union (IoU) scores in [13].
However, these methods may fail and lead to severe degeneration of localization estimation
when the ground-truth of bounding boxes is ambiguous. To jointly handle these two problems,
we adopt the well-designed strategies in [8] to learn the distributions of bounding boxes and
use the statistics of regression offsets for the localization quality estimation. Then, Generalized
Focal Loss [14] is used for the joint optimization of classification and regression, which eases
the inconsistency skillfully and results in a significant improvement of detection accuracy.
2.1.3. Improving Generalization Ability
In addition to augmenting training data offline, improving the diversity of training samples
in each image has great potential to promote the generalization ability of neural networks.
Therefore, the allocation of training samples plays a decisive role in the model optimization for
polyp detection, especially in the case of insufficient and high-variance EndoCV 2021 endoscopic
Figure 3: The overview of the segmentation model with HRNet backbone [10] and the proposed low-
rank module.
datasets. Previous works tend to use hand-craft IoU thresholds (Faster RCNN [2], RetinaNet [15],
etc.) and spatial constraints (FCOS [12], etc.) to select training samples, which may bring about
the biased optimization for object detectors. Besides, in most FPN-based paradigms, training
samples are allocated to different levels of feature pyramid layers according to their scales
manually, leading to optimization difficulties. To relieve these problems, we utilize the novel
ATSS [9] strategy to select high-quality training samples adaptively, which fully utilizes the
statistics information of anchors. Nevertheless, the gap between endoscopic and natural scenes is
still obvious and significant, which inspires us to adjust the number of selected positive samples
to fit the endoscopic scenarios and improve the generalization ability of neural networks. Some
key properties of endoscopic scenes for polyp detection are concluded as followed,
β’ Non-overlapping: the overlapping of polyps is extremely rare.
β’ Large-scale: the scales of polyps tend to be larger than 96Γ96 pixels shown in Figure 2
Right, which are defined as large objects using MS COCO evaluation matrix.
β’ Sparsity: the polyps in each image tend to be sparse.
β’ High-variance: high inter-sample variance is caused by different data sources.
To apply the strategy to the endoscopic scenarios and improve the generalization ability for
endoscopic polyp detection, we enlarge the number of samples for each instance from k=9 to
k=13 to increase the diversity of data in an online manner. The increasing number of samples
wonβt generate many intolerable low-quality allocations thanks to the properties of sparsity,
non-overlapping, and large-scale, but achieves the instance-level augmentation with feature
representations in turn.
2.2. Method Details for Polyp Segmentation
We utilize HRNet as our backbone for polyp segmentation (Β§2.2.1), and then propose a low-rank
module (Β§2.2.2) to enhance model generalization. Cross entropy and dice loss are utilized to
optimize the whole model (Β§2.2.3). The whole framework is shown in Figure 3.
Figure 4: Illustration of the instance Resolution Distribution. Left: Instance scale statistic. Right:
Instance ratio of height to width.
2.2.1. Backbone Selection
To choose a suitable solution for the polyp segmentation in EndoCV 2021 challenge, we conduct
a detailed analysis of the dataset at the instance level. We regard the size in the range of 0-400
as small instances, 400-800 as middle instances, and above 800 as large instances. According to
the polyp size statistics in Figure 4 Left, we find that small polyps are the majority ones. Figure
4Right analyzes the ratio of high to width for the polyps, showing that the ratio distributes
widely. When feature representations become low-resolution inner the backbone, itβs hard
to recognize the small instances, especially with biased ratios. Considering these, we adopt
HRNet [10] as our backbone network, which can maintain the high-resolution representations
among the whole process. Two main components of this backbone are parallel inference and
information fusion, as shown in Figure 3.
Parallel Inference The main idea of parallel inference is to perform convolution operations
in three different resolutions, i.e., the blue lines in Figure 3. In this way, the high-resolution
branch can keep the detailed information, and the low-resolution branch can grasp the semantic
information over a wide range of regions.
Information fusion However, the high-resolution branch has difficulty in learning large
patterns such as a pedunculated polyp, and the low-resolution branch canβt learn detailed
information such as hemorrhage polyp. Information fusion is utilized to address this problem,
i.e., the red lines in Figure 3. Before each cross-branch information fusion, 2Γ down-sampling
or 2Γ up-sampling is performed to ensure the strict resolution match. Then, concatenation is
adopted to fuse multi-level information in each branch. At last, we resize the feature maps to
the raw resolution and concatenate them, followed by 1 Γ 1 convolution to generate πΉπ .
2.2.2. Low-rank Module
In order to further eliminate noisy information in πΉπ and enhance model generalization, we
propose a low-rank module to project the feature map πΉπ into a set of low-rank bases and
reconstruct a low-rank feature map πΉπ to predict the final result. Specifically, we reshape the
feature map πΉπ to π Γ πΆ, where π = π π» is the number of pixels and πΆ is the channel number.
To compress the semantic information, πΉπ is embedded into a low-dimension space of π· free
degree using an affine function π(Β·) with learned parameters, followed by a softmax function,
πΉπ = π ππ π‘πππ₯(π(πΉπ )) β π
π Γπ· . Then, low-rank bases are calculated by π = N1 πΉππ πΉπ β
π
πΆΓπ· , where N represents the normalized coefficient and π· is a predefined number. Each base
represents the concentrated semantic information of each degree in low-dimension space. In
the end, the low-rank feature map πΉπ is reconstruct by πΉπ = πΉπ ππ . In general, the rank of πΉπ
is πππ{π, πΆ}, empirically 1k, and the rank of πΉπ is less than π·. With this low-rank module,
the feature map in the high dimensional space is redistributed to a low dimensional manifold,
which removes unnecessary information and enhances the model generalization.
2.2.3. Optimization objective
We employ two supervised losses, cross entropy and dice βοΈloss, to supervise the learning of
π
πΉπ and πΉπ . Cross-entropy loss is formulated as πΏπΆπΈ = π=1 π¦π log ππ , where N is the pixel
number of the whole image, π¦π is the one-hot ground truth and ππ is the prediction probability.
Dice-loss measures βοΈ
the overlap between the predicted region and ground truth, which is defined
π
π¦π ππ
as πΏπ· = 1 β 2 βοΈ π=1
βοΈ π .
πβπΏ π=1 (π¦π +ππ )
3. Experiments
3.1. Experiments for Polyp Detection
Experiment Setting. To perform model selection and method verification, we conduct exten-
sive experiments on the released training data [16] with 80% for training (1062 images) and 20%
for validation (266 images) before offline data augmentation, while the final model is trained
using all data. Pretrained on ImageNet, we further train our models with SGD using 2 NVIDIA
V100 GPUs with a batch-size 8 for 24 epochs (2Γ training schedule). The learning rate is set
0.01 and decreased by 10 at epoch 16 and 22. The Average Precision (AP) is calculated with
linear IoU thresholds from .5 to .95 with 0.05 interval. For the final model used in EndoCV 2021
polyp detection challenge, multi-scale training is performed by randomly re-scaling images
from (1333, 480) to (1333, 960) with 120 intervals. We not only use the common online data
augmentation strategies, e.g., randomly cropping and flipping with 50% probability, but also
use some offline augmentation methods, such as random rotation (75% probability to rotate
arbitrary angle), gamma contrast (πΎ β [0.5, 2.0]), and brightness transformation with a random
value from -10 to 10, etc. The NMS threshold is set 0.01, and the score-threshold is set 0.3 for
lower AP and dev or 0 for higher AP and dev, which can be viewed as a trade-off between
robustness and accuracy.
Baseline Selection. In some scenes, well-designed one-stage detectors [9, 13, 8] have achieved
higher detection performance and shown more potential on inference speed. Instead of rashly
choosing two-stage, cascade, or ensemble pipelines, we perform extensive experiments on
the baseline selection shown in Table 1. As we expected, one-stage baselines show absolute
advantages in polyp detection. This is because RPN will degenerate into an inefficient single-
stage detector when the number of categories is small. Therefore, we choose one-stage detector
Method AP AP50 AP75 APπ APπ APπ
FRCNNβ [11] 46.8 68.1 52.6 12.5 28.2 48.7
One-stage Two-stage
Cascade RCNNβ [3] 49.3 69.0 55.4 10.4 23.9 51.6
Double-Head-Ext [17] 48.0 69.2 52.4 12.5 25.2 50.3
D2Det [7] 48.2 75.3 56.2 5.5 36.0 49.9
RetinaNetβ [15] 46.6 68.1 51.6 9.2 26.6 48.1
FCOSβ [12] 48.1 74.7 50.6 7.6 27.6 50.1
PAA [13] 51.8 77.5 56.1 12.3 25.2 54.5
ATSS [9] 49.9 72.1 56.1 10.4 28.4 52.0
Table 1
Comparison results (%) of two-stage and one-stage detection frameworks on validation set using
ResNet-50 backbone with 2Γ training schedule. β represents the optional baseline models.
Backbone Necks DCN MSπ‘ππππ MSπ‘ππ π‘ AP AP50 AP75 APπ APπ APπ
ResNet-50 FPN β 51.0 73.5 56.8 12.5 28.3 53.5
ResNet-50 FPN β β 53.1 75.8 59.9 14.6 30.6 55.3
ResNet-50 FPN β β 53.8 76.6 57.8 12.3 27.3 56.0
ResNet-101 FPN β β 55.0 77.3 62.4 18.0 36.0 58.4
ResNeXt-101 FPN β β β 56.3 76.9 63.5 12.5 33.0 58.5
ResNeXt-101 FPN 52.1 73.1 60.9 14.2 33.0 53.1
β β
ResNeXt-101 PAFPN [18] β β 53.3 76.1 60.6 12.5 32.2 55.2
ResNeXt-101 BiFPN [19] 55.8 77.0 62.2 15.8 32.9 57.2
Table 2
Comparison results (%) of different feature extractors in our model. MSπ‘ππππ and MSπ‘ππ π‘ indicate multi-
scale training and test strategies.
FCOS [12] as our baseline.
Investigation on Feature Extractors. For polyp detection in EndoCV2021 challenge, heavier
backbones may not be suitable for small-scale datasets [16] due to the potential over-fitting of
neural networks. Fortunately, we find a consistent improvement as increasing the scale of neural
networks, as shown in Table 2, which demonstrates the high-variance of data distributions can
reduce the possibility of over-fitting. On the contrary, utilizing stronger multi-scale feature
fusion methods, e.g., PAFPN [18] and BiFPN [19], doesnβt improve the performance due to the
biased scale distribution. Besides, performing multi-scale inference leads to significantly AP
drops, as demonstrated in Table 2.
Ablation Analysis. As shown in Table 3, we perform ablation analysis on each component
using our validation set. Compared with the FCOS baseline, introducing ATSS [9] can achieve
1.8 AP improvement, which demonstrates the effectiveness of the sample selection strategy.
After relieving the influence of OI and DI, a significant 2.9 AP improvement can be achieved
by introducing both ATSS and GFL v2 [8] together with the comparison of our baseline. In
addition to achieving state-of-the-art performance on endoscopic polyp detection, our model
also has obvious advantages in the inference speed because of the one-stage detection pipeline.
Method AP AP50 AP75 APπ APπ APπ
FCOS (baseline) [12] 48.1 74.7 50.6 7.6 27.6 50.1
FCOS+ATSS [9] 49.9 72.1 56.1 10.4 28.4 52.0
FCOS+ATSS+GFLv2 [8] 51.0 73.5 56.8 12.5 28.3 53.5
Table 3
Ablation study results (%) on validation set using ResNet-50 backbone with 2Γ training schedule.
Method Dice (%) Spe (%) Sen (%) Acc (%) IoUπ (%) IoUπ (%) mIoU (%)
UNet++ [4] 66.067 99.339 72.788 96.939 59.552 96.797 78.175
PraNet [5] 61.822 97.444 79.383 95.937 53.300 95.688 74.494
ACSNet [20] 72.708 98.903 77.753 97.463 66.691 97.285 81.988
ACFNet [21] 76.204 99.369 76.204 98.084 70.491 97.960 84.225
HRNet [10] 88.351 98.839 93.224 98.496 79.133 98.405 88.769
HRNet+Low-rank 90.364 99.007 96.288 98.856 82.422 98.791 90.607
Table 4
Comparison with state-of-the-art polyp segmentation methods.
3.2. Experiments for Polyp Segmentation
Experiment setting. To evaluate our method on the released training data [16], we first split
them into 80% for training (1062 images) and 20% for testing (266 images). Images are resized to
512Γ512 pixels. We apply augmentation techniques upon images: random flipping and rotation
with 50% probability, color shift (brightness, color, sharpness, and contrast), and Gaussian noise
N (0.2, 0.3). At last, we normalize these images into [-1, 1]. The backbone of the segmentation
model is HRNetV2 with parameters initialized on ImageNet. We utilize the SGD optimizer
with the base learning rate of 0.01, the momentum of 0.9, and the weight decay of 0.0005. All
experiments are implemented by the Pytorch framework and trained on four parallel Nvidia
GeForce 2080Ti GPUs with a batch-size of 16 for 484 epochs. To evaluate the performance
of polyp segmentation, seven common criteria including Dice Score (Dice), Sensitivity (Sen),
Specificity (Spe), Accuracy (Acc), IoU of polyp regions (IoUπ ), IoU of backgrounds (IoUπ ) and
Mean IoU (mIoU) are utilized.
Comparison with State-of-the-art Methods. To verify the effectiveness of our method,
we perform a comprehensive comparison with state-of-the-art polyp segmentation methods,
including UNet++ [4], PraNet [5], ACSNet [20] and ACFNet [21], as shown in Table 4. Specifically,
our method achieves the best performance, with dice score of 90.364% and mIoU of 90.607%,
demonstrating the superiority of our method over state-of-the-art polyp segmentation methods.
In EndoCV 2021 polyp segmentation challenge, our segmentation model achieves 0.7771 Β± 0.0695
score and ranked 4th place based on EndoCV metrics that included generalisation deviation
scores between test sets [22].
4. Conclusion
Automatic polyp detection and segmentation are challenging due to the collected heterogeneous
dataset. We find OI and DI as two major limitations for high-quality polyp detection for the polyp
detection task. To handle these issues, we jointly optimize classification and regression to bridge
OI and use the regression offset distribution to relieve DI. To further promote the generalization
ability of neural networks, we utilize ATSS to improve the diversity of training samples in each
image. For the polyp segmentation task, we find the small polyps make up the majority of the
dataset. Hence we exploit HRNet as the backbone. To enhance the generalization of the model,
we propose the low-rank module. Extensive experiments demonstrate the effectiveness of our
methods. In the future, we aim to integrate the detection and segmentation framework for
high-quality polyp instance segmentation.
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