Deformable Dilated Faster R-CNN for Universal Lesion Detection in CT Images

Hellmann, Fabio; Ren, Zhao; André, Elisabeth; Schuller, Björn W.

doi:10.1109/embc46164.2021.9631021

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Although the research in this paper has achieved some results, the following areas need further exploration in the future: (1) We consider accelerating the convolution operation and optimizing the loss function to improve the performance of our models. (2) Deformable convolution is used to enhance the transformation modeling capability of CNNs [29][30][31][32][33], and it should be determined whether adding it to the model can enhance the feature extraction capability.…”

Section: Discussionmentioning

confidence: 99%

A Medical Image Segmentation Method Based on Improved UNet 3+ Network

Hou

Wang

et al. 2023

Diagnostics

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In recent years, segmentation details and computing efficiency have become more important in medical image segmentation for clinical applications. In deep learning, UNet based on a convolutional neural network is one of the most commonly used models. UNet 3+ was designed as a modified UNet by adopting the architecture of full-scale skip connections. However, full-scale feature fusion can result in excessively redundant computations. This study aimed to reduce the network parameters of UNet 3+ while further improving the feature extraction capability. First, to eliminate redundancy and improve computational efficiency, we prune the full-scale skip connections of UNet 3+. In addition, we use the attention module called Convolutional Block Attention Module (CBAM) to capture more essential features and thus improve the feature expression capabilities. The performance of the proposed model was validated by three different types of datasets: skin cancer segmentation, breast cancer segmentation, and lung segmentation. The parameters are reduced by about 36% and 18% compared to UNet and UNet 3+, respectively. The results show that the proposed method not only outperformed the comparison models in a variety of evaluation metrics but also achieved more accurate segmentation results. The proposed models have lower network parameters that enhance feature extraction and improve segmentation performance efficiently. Furthermore, the models have great potential for application in medical imaging computer-aided diagnosis.

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Section: Discussionmentioning

confidence: 99%

A Medical Image Segmentation Method Based on Improved UNet 3+ Network

Hou

Wang

et al. 2023

Diagnostics

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DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence ^*

Cheng,

Zhao,

Liu

et al. 2024

Phys. Med. Biol.

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Objective: This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities.Approach: To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting. We propose a novel deformable ConvLSTM module that integrates a deformable mechanism with ConvLSTM, enabling the model to adapt to data with varying structures. Furthermore, we introduce the deformable multi-sequence guidance model (DMGM) as an auxiliary diagnostic tool for cervical cancer staging.Main Results: Through extensive testing, including comparative and ablation studies, we validate the effectiveness of the deformable ConvLSTM module and the DMGM. Our findings highlight the model's ability to adapt to the deformation mechanism and address the challenges in cervical cancer tumor staging, thereby overcoming the overfitting issue and ensuring the synchronization of asynchronous scan sequences. The research also utilized the multi-modal data from BraTS 2019 as an external test dataset to validate the effectiveness of the proposed methodology presented in this study.Significance: The DMGM represents the first deep learning model to analyze multiple MRI sequences for cervical cancer, demonstrating strong generalization capabilities and effective staging in small dataset scenarios. This has significant implications for both deep learning applications and medical diagnostics. The source code will be made available subsequently.

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