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Minimal-Supervised Medical Image Segmentation via Vector Quantization Memory 1

26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Xu, Yanyu Zhou, Menghan Feng, Yangqin Xu, Xinxing Fu, Huazhu Goh, Rick Siow Mong Liu, Yong ASTAR Inst High Performance Comp IHPC 1 Fusionopolis Way16-16 Connexis Singapore 138632 Singapore

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Medical imaging segmentation is a critical key task for computer-assisted diagnosis and disease monitoring. However, collecting a large-scale medical dataset with well-annotation is time-consuming and requires domain knowledge. Reducing the number of annotations poses two challenges: obtaining sufficient supervision and generating high-quality pseudo labels. To address these, we propose a universal framework for annotation-efficient medical segmentation, which is capable of handling both scribble-supervised and point-supervised segmentation. Our approach includes an auxiliary reconstruction branch that provides more supervision and backwards sufficient gradients for learning visual representations. Besides, a novel pseudo label generation branch utilizes the Vector Quantization (VQ) bank to store texture-oriented and global features for generating pseudo labels. To boost the model training, we generate the high-quality pseudo labels by mixing the segmentation prediction and pseudo labels from the VQ bank. The experimental results on the ACDC MRI segmentation dataset demonstrate effectiveness of our designed method. We obtain a comparable performance (0.86 vs. 0.87 DSC score) with a few points.

关键词： annotation-efficient learning Vector Quantization

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Understanding the impact of image and input resolution on deep digital pathology patch classifiers 19

Understanding the impact of image and input resolution on de...

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19th Conference on Robots and Vision (CRV)

作者： Teh, Eu Wern Taylor, Graham W. Univ Guelph Sch Engn Vector Inst Artificial Intelligence Guelph ON Canada

ISBN: (纸本)9781665497749

We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on DP patch classification performance. We use two cancer patch classification datasets PCam and CRC, to validate the results of our study. Our experiments show that patch classification performance can be improved by manipulating both the image and input resolution in annotation-scarce and annotation-rich environments. We show a positive correlation between the image and input resolution and the patch classification accuracy on both datasets. By exploiting the image and input resolution, our final model trained on < 1% of data performs equally well compared to the model trained on 100% of data in the original image resolution on the PCam dataset.

关键词： Digital Pathology Patch Classification annotation-efficient learning

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Facing annotation Redundancy: OCT Layer Segmentation with only 10 Annotated Pixels per Layer 1

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1st International Workshop on Resource-efficient Medical Image Analysis (REMIA) / 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Xu, Yanyu Xu, Xinxing Fu, Huazhu Wang, Meng Goh, Rick Siow Mong Liu, Yong ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

The retinal layer segmentation from OCT images is a fundamental and important task in the diagnosis and monitoring of eye-related diseases. The quest for improved accuracy is driving the use of increasingly large dataset with fully pixel-level layer annotations. But the manual annotation process is expensive and tedious, further, the annotators also need sufficient medical knowledge which brings a great burden on the doctors. We observe that there exist a large number of repetitive texture patterns in the flatten OCT images. More surprisingly, by significantly reducing the annotation from 100% to 10%, even to 1%, the performance of a segmentation model only drops a little, i.e., error from 2.53 mu m to 2.76 mu m, and to 3.27 mu m on a validation set, respectively. Such observation motivates us to deeply investigate the redundancies of the annotation in the feature space which would definitely facilitate the annotation for medical images. To greatly reduce the expensive annotation costs, we propose a new annotation-efficient learning paradigm by annotating a fixed and limited number of pixels for each layer in each image. Considering the redundancies in the repetitive patterns in each layer of OCT images, we employ a VQ memory bank to store the extracted features on the whole datasets to augment the visual representation. The experimental results on two public datasets validate the effectiveness of our model. With only 10 annotated pixels for each layer in an image, our performance is very close to the previous methods trained with the whole fully annotated dataset.

关键词： OCT layer segmentation annotation-efficient learning

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Semi-supervised Active Salient Object Detection

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PATTERN RECOGNITION 2022年 123卷 108364-108364页

作者： Lv, Yunqiu Liu, Bowen Zhang, Jing Dai, Yuchao Li, Aixuan Zhang, Tong Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China Australian Natl Univ Canberra ACT Australia Ecole Polytech Fed Lausanne Lausanne Switzerland

In this paper, we propose a novel semi-supervised active salient object detection (SOD) method that actively acquires a small subset of the most discriminative and representative samples for labeling. Two main contributions have been made to prevent the method from being overwhelmed by labeling similar distributed samples. First, we design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. Then, we select the least confident (discriminative) samples from the unlabeled pool to form the "candidate labeled pool". Second, we train a Variational Auto-Encoder (VAE) to select and add the most representative data from the "candidate labeled pool" into the labeled pool by comparing their corresponding features in the latent space. Within our framework, these two networks are optimized conditioned on the states of each other progressively. Experimental results on six benchmarking SOD datasets demonstrate that our annotation efficient learning based salient object detection method, reaching to 14% labeling budget, can be on par with the state-of-the-art fully-supervised deep SOD models. The source code is publicly available via our project page: https://***/JingZhang617/Semi- sup- active-selfsup-learning . (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Salient object detection annotation-efficient learning Active learning Variational Auto-Encoder

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