ADS_UNet: A nested UNet for histopathology image segmentation

Yang, Yilong; Dasmahapatra, Srinandan; Mahmoodi, Sasan

doi:10.1016/j.eswa.2023.120128

Expert Systems with Applications

2023

DOI: 10.1016/j.eswa.2023.120128

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ADS_UNet: A nested UNet for histopathology image segmentation

Yilong Yang¹,

Srinandan Dasmahapatra²,

Sasan Mahmoodi³

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Cited by 15 publications

(1 citation statement)

References 28 publications

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“…The ConvDeconvNet model is renowned for its exceptional performance in object recognition tasks, particularly in challenging scenarios characterized by low-lighting conditions and complex backgrounds. In contrast, UNET (Yang et al, 2023) demonstrates notable computational efficiency, rendering it a compelling option for use cases necessitating immediate image processing.…”

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confidence: 99%

Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection

Hindarto

2023

SinkrOn

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The precise identification and localization of fish entities within visual data is essential in diverse domains, such as marine biology and fisheries management, within computer vision. This study provides a thorough performance evaluation of two prominent deep learning algorithms, ConvDeconvNet and UNET, in the context of fish object detection. Both models are assessed using a dataset comprising a wide range of fish species, considering various factors, including accuracy of detection, speed of processing, and complexity of the model. The findings demonstrate that ConvDeconvNet exhibits superior performance in terms of detection accuracy, attaining a noteworthy degree of precision and recall in identifying fish entities. In contrast, the UNET model displays a notable advantage in terms of processing speed owing to its distinctive architectural design, rendering it a viable option for applications requiring real-time performance. The discourse surrounding the trade-off between accuracy and speed is examined, offering valuable perspectives for algorithm selection following specific criteria. Furthermore, this study highlights the significance of incorporating a diverse range of datasets for training and testing purposes when utilizing these models, as it significantly influences their overall performance. This study makes a valuable contribution to the continuous endeavors to improve the detection of fish objects in underwater images. It provides a thorough evaluation and comparison of ConvDeconvNet and UNET, thereby assisting researchers and practitioners in making well-informed decisions regarding selecting these models for their specific applications.

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confidence: 99%

Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection

Hindarto

2023

SinkrOn

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CLAC-Net: a composite medical image segmentation framework using self-attention and cross-layer asymmetric connections

Feng,

Wang,

Xue

et al. 2024

Vis Comput

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Bi-VLGM: Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation

Chen,

Liu,

Liu

et al. 2024

Int J Comput Vis

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Medical reports containing specific diagnostic results and additional information not present in medical images can be effectively employed to assist image understanding tasks, and the modality gap between vision and language can be bridged by vision-language matching (VLM). However, current vision-language models distort the intra-model relation and only include class information in reports that is insufficient for segmentation task. In this paper, we introduce a novel Bi-level class-severity-aware Vision-Language Graph Matching (Bi-VLGM) for text guided medical image segmentation, composed of a word-level VLGM module and a sentence-level VLGM module, to exploit the class-severity-aware relation among visual-textual features. In word-level VLGM, to mitigate the distorted intra-modal relation during VLM, we reformulate VLM as graph matching problem and introduce a vision-language graph matching (VLGM) to exploit the high-order relation among visual-textual features. Then, we perform VLGM between the local features for each class region and class-aware prompts to bridge their gap. In sentence-level VLGM, to provide disease severity information for segmentation task, we introduce a severity-aware prompting to quantify the severity level of disease lesion, and perform VLGM between the global features and the severity-aware prompts. By exploiting the relation between the local (global) and class (severity) features, the segmentation model can include the class-aware and severity-aware information to promote segmentation performance. Extensive experiments proved the effectiveness of our method and its superiority to existing methods. The source code will be released.

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ADS_UNet: A nested UNet for histopathology image segmentation

Cited by 15 publications

References 28 publications

Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection

Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection

CLAC-Net: a composite medical image segmentation framework using self-attention and cross-layer asymmetric connections

Bi-VLGM: Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation

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