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Acquiring weak annotations for Tumor Localization in Temporal and Volumetric Data

Machine Intelligence Research 2024年第2期21卷 318-330页

作者： Yu-Cheng Chou Bowen Li Deng-Ping Fan Alan Yuille Zongwei Zhou Department of Computer Science Johns Hopkins UniversityBaltimore 21218USA Computer Vision Lab ETH ZurichZurich 8001Switzerland

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and ***,with limited resources,it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled *** address this issue,we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans;Both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data,involving either temporary or spatial *** this paper,we develop a new annotation strategy,termed Drag&Drop,which simplifies the annotation process to drag and *** annotation strategy is more efficient,particularly for temporal and volumetric imaging,than other types of weak annotations,such as per-pixel,bounding boxes,scribbles,ellipses and ***,to exploit our Drag&Drop annotations,we develop a novel weakly supervised learning method based on the watershed *** results show that our method achieves better detection and localization performance than alternative weak annotations and,more importantly,achieves similar performance to that trained on detailed per-pixel ***,we find that,with limited resources,allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of *** summary,this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.

关键词： weak annotation detection localization segmentation colonoscopy abdomen

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G2MF-WA:Geometric multi-model fitting with weakly annotated data

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Computational Visual Media 2020年第2期6卷 135-145页

作者： Chao Zhang Xuequan Lu Katsuya Hotta Xi Yang University of Fukui Fukui910-8507Japan Deakin University Waurn Ponds3216Australia The University of Tokyo Tokyo113-8656Japan

In this paper we address the problem of geometric multi-model fitting using a few weakly annotated data points,which has been little studied so *** weak annotating(WA),most manual annotations are supposed to be correct yet inevitably mixed with incorrect *** WA data can naturally arise through interaction in various *** example,in the case of homography estimation,one can easily annotate points on the same plane or object with a single label by observing the *** by this,we propose a novel method to make full use of WA data to boost multi-model fitting ***,a graph for model proposal sampling is first constructed using the WA data,given the prior that WA data annotated with the same weak label has a high probability of belonging to the same *** incorporating this prior knowledge into the calculation of edge probabilities,vertices(i.e.,data points)lying on or near the latent model are likely to be associated and further form a subset or cluster for effective proposal *** generated proposals,α-expansion is used for labeling,and our method in return updates the *** procedure works in an iterative *** experiments validate our method and show that it produces noticeably better results than state-of-the-art techniques in most cases.

关键词： geometric multi-model fitting weak annotation multi-homography detection two-view motion segmentation

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