SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations

Cheng, Jijun; Feihu, Hou,; Lan, Rushi; Lu, Cheng; Li, Lingqiao; Pan, Xipeng; Wang, Huadeng; Liang, Changhong; Liu, Zhenbing; Chen, Xin; Han, Chu; Liu, Zaiyi

doi:10.1016/j.media.2023.102867

Cited by 22 publications

(9 citation statements)

References 40 publications

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“…The detecting performance of the detection algorithm can be evaluated using the Receiver Operating Characteristic (ROC) curve [28], which reflects the Detection Rate (DR) [28] and False Alarm Rate (FAR) [28] in the threshold's varying condition. The DR and the FAR can be calculated by Equation (15). As the image engineering concept of this paper is to identify all real maritime targets while allowing a small number of maritime interference regions to be identified as false detected targets, the primary goal is to ensure a high value for DR. Then, we need to make efforts to reduce FAR, which is to say that DR is more important than FAR.…”

Section: The Target Detection Results Of Lcmdsm Algorithm and Compare...mentioning

confidence: 99%

“…The Recursive Separated and Weighted Histogram Equalization (RSWHE) [14] algorithm splits an input histogram into more than two sub-histograms recursively based on the mean or median of the sub-image to modify the sub-histogram by a normalized power-law function and then equalizes the weighted sub-histogram. It can keep remarkable brightness and enhances the contrast but easily produces artifacts [15,16] in the image.…”

Section: Introductionmentioning

confidence: 99%

“…Figure AG EME Figure AG EME Figure 2a 1.11 1.30 Figure 2b 0.86 1.95 Figure13a1.67 1.86 Figure14a2.84 2.79 Figure13b1.46 1.71 Figure14b1.22 2.72 Figure13c1.33 1.82 Figure14c1.33 1.82 Figure13d3.34 2.30 Figure14d4.26 3 15. …”

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confidence: 99%

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An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

et al. 2023

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Aiming at maritime infrared target detection with low contrast influenced by maritime clutter and illumination, this paper proposes a Modified Histogram Equalization with Edge Fusion (MHEEF) pre-processing algorithm in backlight maritime scenes and establishes Local-Contrast Saliency Models with Double Scale and Modes (LCMDSM) for detecting a target with the properties of positive and negative contrast. We propose a local-contrast saliency mathematical model with double modes in the extension of only one mode. Then, the big scale and small scale are combined into one Target Detection Unit (TDU), which can approach the “from bottom to up” mechanism of the Visual Attention Model (VAM) better and identify the target with a suitable size, approaching the target’s actual shape. In the experimental results and analysis, clutter, foggy, backlight, and dim maritime scenes are chosen to verify the effectiveness of the target detection algorithm. From the enhancement result, the LCMDSM algorithm can achieve a Detection Rate (DR) with a value of 98.26% under each maritime scene on the average level and can be used in real-time detection with low computational cost.

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Section: The Target Detection Results Of Lcmdsm Algorithm and Compare...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

et al. 2023

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“…The advent of computer technology has spurred the advancement of automated analysis of whole-slide images (WSIs). Deep learning (DL) technology has the potential to extract image features automatically providing a valuable tool for quantitative analysis of WSIs [ 18 ], information mining [ 19 ], and prognostic analysis [ 20 ]. This technology has the potential to automate the quantification of TILs in WSIs.…”

Section: Introductionmentioning

confidence: 99%

Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma

Pan,

Feng,

Wang

et al. 2024

Heliyon

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“…Infrared and visible image fusion (IVF) has gained significant attention and is widely used in various applications [ 1 ]. Specifically, the effective fusion of shared and specific features from different modalities enables the generation of high-quality fused images, which, in turn, benefits downstream tasks, such as object detection [ 2 , 3 , 4 ], medical image processing [ 5 , 6 ], semantic segmentation [ 7 , 8 , 9 ], and pedestrian detection [ 10 , 11 ]. Although IVF has received much attention in various applications, IVF remains challenging due to the significant differences in appearance between these two image types.…”

Section: Introductionmentioning

confidence: 99%

DPACFuse: Dual-Branch Progressive Learning for Infrared and Visible Image Fusion with Complementary Self-Attention and Convolution

Zhu

Wang

et al. 2023

Sensors

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Infrared and visible image fusion aims to generate a single fused image that not only contains rich texture details and salient objects, but also facilitates downstream tasks. However, existing works mainly focus on learning different modality-specific or shared features, and ignore the importance of modeling cross-modality features. To address these challenges, we propose Dual-branch Progressive learning for infrared and visible image fusion with a complementary self-Attention and Convolution (DPACFuse) network. On the one hand, we propose Cross-Modality Feature Extraction (CMEF) to enhance information interaction and the extraction of common features across modalities. In addition, we introduce a high-frequency gradient convolution operation to extract fine-grained information and suppress high-frequency information loss. On the other hand, to alleviate the CNN issues of insufficient global information extraction and computation overheads of self-attention, we introduce the ACmix, which can fully extract local and global information in the source image with a smaller computational overhead than pure convolution or pure self-attention. Extensive experiments demonstrated that the fused images generated by DPACFuse not only contain rich texture information, but can also effectively highlight salient objects. Additionally, our method achieved approximately 3% improvement over the state-of-the-art methods in MI, Qabf, SF, and AG evaluation indicators. More importantly, our fused images enhanced object detection and semantic segmentation by approximately 10%, compared to using infrared and visible images separately.

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SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations

Cited by 22 publications

References 40 publications

An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma

DPACFuse: Dual-Branch Progressive Learning for Infrared and Visible Image Fusion with Complementary Self-Attention and Convolution

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