3 research outputs found
Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features
Diabetic retinopathy (DR) is a diabetes complication that affects eyes. DR is
a primary cause of blindness in working-age people and it is estimated that 3
to 4 million people with diabetes are blinded by DR every year worldwide. Early
diagnosis have been considered an effective way to mitigate such problem. The
ultimate goal of our research is to develop novel machine learning techniques
to analyze the DR images generated by the fundus camera for automatically DR
diagnosis. In this paper, we focus on identifying small lesions on DR fundus
images. The results from our analysis, which include the lesion category and
their exact locations in the image, can be used to facilitate the determination
of DR severity (indicated by DR stages). Different from traditional object
detection for natural images, lesion detection for fundus images have unique
challenges. Specifically, the size of a lesion instance is usually very small,
compared with the original resolution of the fundus images, making them
diffcult to be detected. We analyze the lesion-vs-image scale carefully and
propose a large-size feature pyramid network (LFPN) to preserve more image
details for mini lesion instance detection. Our method includes an effective
region proposal strategy to increase the sensitivity. The experimental results
show that our proposed method is superior to the original feature pyramid
network (FPN) method and Faster RCNN.Comment: diabetic retinopathy, mini lesion detection, FP
Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
Diabetic retinopathy (DR) is a primary cause of blindness in working-age
people worldwide. About 3 to 4 million people with diabetes become blind
because of DR every year. Diagnosis of DR through color fundus images is a
common approach to mitigate such problem. However, DR diagnosis is a difficult
and time consuming task, which requires experienced clinicians to identify the
presence and significance of many small features on high resolution images.
Convolutional Neural Network (CNN) has proved to be a promising approach for
automatic biomedical image analysis recently. In this work, we investigate
lesion detection on DR fundus images with CNN-based object detection methods.
Lesion detection on fundus images faces two unique challenges. The first one is
that our dataset is not fully labeled, i.e., only a subset of all lesion
instances are marked. Not only will these unlabeled lesion instances not
contribute to the training of the model, but also they will be mistakenly
counted as false negatives, leading the model move to the opposite direction.
The second challenge is that the lesion instances are usually very small,
making them difficult to be found by normal object detectors. To address the
first challenge, we introduce an iterative training algorithm for the
semi-supervised method of pseudo-labeling, in which a considerable number of
unlabeled lesion instances can be discovered to boost the performance of the
lesion detector. For the small size targets problem, we extend both the input
size and the depth of feature pyramid network (FPN) to produce a large CNN
feature map, which can preserve the detail of small lesions and thus enhance
the effectiveness of the lesion detector. The experimental results show that
our proposed methods significantly outperform the baselines
3D Aggregated Faster R-CNN for General Lesion Detection
Lesions are damages and abnormalities in tissues of the human body. Many of
them can later turn into fatal diseases such as cancers. Detecting lesions are
of great importance for early diagnosis and timely treatment. To this end,
Computed Tomography (CT) scans often serve as the screening tool, allowing us
to leverage the modern object detection techniques to detect the lesions.
However, lesions in CT scans are often small and sparse. The local area of
lesions can be very confusing, leading the region based classifier branch of
Faster R-CNN easily fail. Therefore, most of the existing state-of-the-art
solutions train two types of heterogeneous networks (multi-phase) separately
for the candidate generation and the False Positive Reduction (FPR) purposes.
In this paper, we enforce an end-to-end 3D Aggregated Faster R-CNN solution by
stacking an "aggregated classifier branch" on the backbone of RPN. This
classifier branch is equipped with Feature Aggregation and Local Magnification
Layers to enhance the classifier branch. We demonstrate our model can achieve
the state of the art performance on both LUNA16 and DeepLesion dataset.
Especially, we achieve the best single-model FROC performance on LUNA16 with
the inference time being 4.2s per processed scan