Bounding Maps for Universal Lesion Detection

Han, Li; Han, Hu; Zhou, S. Kevin

doi:10.1007/978-3-030-59719-1_41

Cited by 16 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Universal Lesion Detection (ULD) in computed tomography (CT) [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] plays an important role in computer-aided diagnosis (CAD) [19,20]. The design of detection-only instead of identifying the lesion types in ULD [21][22][23][24][25][26][27][28] prominently decreases the difficulty of this task for a specific organ (e.g., lung, liver), but it is still challenging for lesions vary in shapes and sizes among whole human body.…”

Section: Introductionmentioning

confidence: 99%

“…An anchor is considered positive if its IoU with any GT BBox is greater than the IoU threshold and negative otherwise [29]. The positive anchors are sufficient in natural images as they usually have many targets per image [12]. However, the number of lesions per CT scan is limited, most CT slices only contain one or two lesions (i.e., detection targets in ULD) per CT slice [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DiffULD: Diffusive Universal Lesion Detection

Zhao¹,

Li²,

Jin³

et al. 2023

Preprint

View full text Add to dashboard Cite

Universal Lesion Detection (ULD) in computed tomography (CT) plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by anchor-based detection designs, but they have inherent drawbacks due to the use of anchors: i) Insufficient training target and ii) Difficulties in anchor design. Diffusion probability models (DPM) have demonstrated outstanding capabilities in many vision tasks. Many DPM-based approaches achieve great success in natural image object detection without using anchors. But they are still ineffective for ULD due to the insufficient training targets. In this paper, we propose a novel ULD method, DiffULD, which utilizes DPM for lesion detection. To tackle the negative effect triggered by insufficient targets, we introduce a novel center-aligned bounding box padding strategy that provides additional high-quality training targets yet avoids significant performance deterioration. DiffULD is inherently advanced in locating lesions with diverse sizes and shapes since it can predict with arbitrary boxes. Experiments on the benchmark dataset DeepLesion [1] show the superiority of DiffULD when compared to state-of-the-art ULD approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DiffULD: Diffusive Universal Lesion Detection

Zhao¹,

Li²,

Jin³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Medical image learning ( Zhou et al, 2021 ; Qiao et al, 2022 ) is developing rapidly based on the emergence of machine learning ( Song et al, 2021a ; Song et al, 2021b ; Xie et al, 2021 ; Song et al, 2022a ; Song et al, 2022b ; Li et al, 2022 ; Wang et al, 2022 ) and neural network ( Meng et al, 2021a ; Meng et al, 2021b ; Wang et al, 2021 ; Qiao et al, 2022 ), thereby dramatically assists radiologist alleviating workload during reading computed tomography (CT) ( Meng et al, 2022 ) images in computer-aided detection/diagnosis (CADe/CADx) ( Wang et al, 2022 ). Meanwhile, universal lesion detection (ULD) ( Li et al, 2022 ) is an important topic to develop a universal or multicategory CADe/CADx 3D framework, which needs to feed an annotated dataset on computed tomography (CT) ( Yan et al, 2019 ; Li et al, 2020 ; Li et al, 2021 ). However, an exactly annotated dataset is impossible to get because of expensive manual labeling costs with the increasing number of CT images as well as the long-tailed distribution of disease species.…”

Section: Introductionmentioning

confidence: 99%

A universal lesion detection method based on partially supervised learning

Wang,

Shi,

Meng

et al. 2023

Front. Pharmacol.

View full text Add to dashboard Cite

Partially supervised learning (PSL) is urgently necessary to explore to construct an efficient universal lesion detection (ULD) segmentation model. An annotated dataset is crucial but hard to acquire because of too many Computed tomography (CT) images and the lack of professionals in computer-aided detection/diagnosis (CADe/CADx). To address this problem, we propose a novel loss function to reduce the proportion of negative anchors which is extremely likely to classify the lesion area (positive samples) as a negative bounding box, further leading to an unexpected performance. Before calculating loss, we generate a mask to intentionally choose fewer negative anchors which will backward wrongful loss to the network. During the process of loss calculation, we set a parameter to reduce the proportion of negative samples, and it significantly reduces the adverse effect of misclassification on the model. Our experiments are implemented in a 3D framework by feeding a partially annotated dataset named DeepLesion, a large-scale public dataset for universal lesion detection from CT. We implement a lot of experiments to choose the most suitable parameter, and the result shows that the proposed method has greatly improved the performance of a ULD detector. Our code can be obtained at https://github.com/PLuld0/PLuldl.

show abstract

“…Despite that SCN [16] is capable of detecting landmarks in head, hand, and chest datasets, it needs to be trained, which costs more time and storage. Therefore, developing a universal model that detects crossanatomy landmarks is promising and desirable [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objective and Impact Statement . In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on a single dataset, this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark location. Introduction . The accurate and automatic localization of anatomical landmarks plays an essential role in medical image analysis. However, recent deep learning-based methods only utilize limited data from a single dataset. It is promising and desirable to build a model learned from different regions which harnesses the power of big data. Methods . Our model consists of a local network and a global network, which capture local features and global features, respectively. The local network is a fully convolutional network built up with depth-wise separable convolutions, and the global network uses dilated convolution to enlarge the receptive field to model global dependencies. Results . We evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical regions. Extensive experimental results show that our model improves the detection accuracy compared to the state-of-the-art methods. Conclusion . Our model makes the first attempt to train a single network on multiple datasets for landmark detection. Experimental results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.

show abstract

Bounding Maps for Universal Lesion Detection

Cited by 16 publications

References 42 publications

DiffULD: Diffusive Universal Lesion Detection

DiffULD: Diffusive Universal Lesion Detection

A universal lesion detection method based on partially supervised learning

Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

Contact Info

Product

Resources

About