SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

Ullah, Zahid; Usman, Muhammad; Latif, Siddique; Khan, Asifullah; Gwak, Jeonghwan

doi:10.1038/s41598-023-36311-0

Cited by 15 publications

(4 citation statements)

References 56 publications

(74 reference statements)

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“…Furthermore, the introduction of innovative approaches like RTNet by Huang et al [9], hyperbolic space-based Transformer by Wang et al [20], SSMD-Unet by Ullah et al [21], and M2MRF operator by Liu et al [22] has contributed to further advancements in DR lesion segmentation. These methods have leveraged self-attention, cross-attention, relation Transformer blocks, hyperbolic embeddings, and auxiliary reconstruction tasks to enhance segmentation accuracy and address specific challenges associated with scale discrepancy and optimal feature representation.…”

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Bian,

Si,

Wang

2024

Algorithms

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The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability.

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Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Bian,

Si,

Wang

2024

Algorithms

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“…Recently, deep learning (DL)-based techniques have garnered attention across various industrial applications due to their impressive performance [38][39][40]. These applications span object classification [41], segmentation [42][43][44], counting [45], medical image enhancement [46,47], and object detection [48].…”

Section: Related Workmentioning

confidence: 99%

Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs

Kanwal¹,

Rehman²,

Farooq

et al. 2023

Bioengineering

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Teeth segmentation plays a pivotal role in dentistry by facilitating accurate diagnoses and aiding the development of effective treatment plans. While traditional methods have primarily focused on teeth segmentation, they often fail to consider the broader oral tissue context. This paper proposes a panoptic-segmentation-based method that combines the results of instance segmentation with semantic segmentation of the background. Particularly, we introduce a novel architecture for instance teeth segmentation that leverages a dual-path transformer-based network, integrated with a panoptic quality (PQ) loss function. The model directly predicts masks and their corresponding classes, with the PQ loss function streamlining the training process. Our proposed architecture features a dual-path transformer block that facilitates bi-directional communication between the pixel path CNN and the memory path. It also contains a stacked decoder block that aggregates multi-scale features across different decoding resolutions. The transformer block integrates pixel-to-memory feedback attention, pixel-to-pixel self-attention, and memory-to-pixel and memory-to-memory self-attention mechanisms. The output heads process features to predict mask classes, while the final mask is obtained by multiplying memory path and pixel path features. When applied to the UFBA-UESC Dental Image dataset, our model exhibits a substantial improvement in segmentation performance, surpassing existing state-of-the-art techniques in terms of performance and robustness. Our research signifies an essential step forward in teeth segmentation and contributes to a deeper understanding of oral structures.

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“…To increase productivity and the accuracy of diagnoses, computer-aided approaches are replacing traditional medical image analysis techniques. Due to the well-known efficacy of deep learning-based computer-aided diagnosis solutions, deep learning-based medical image analysis is a significant and active research area, with many researchers working in this field [21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Pacal,

Celik,

Bayram

et al. 2024

Cluster Comput

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The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

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SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

Cited by 15 publications

References 56 publications

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

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