WS-MTST: Weakly Supervised Multi-Label Brain Tumor Segmentation With Transformers

Chen, Huazhen; An, Jianpeng; Jiang, Bochang; Xia, Lili; Bai, Yunhao; Gao, Zhongke

doi:10.1109/jbhi.2023.3321602

Cited by 4 publications

(1 citation statement)

References 54 publications

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“…Automatic segmentation of medical images is of great significance for clinical anatomy and pathological structure research including organ segmentation [ 1 ], optic disc segmentation [ 2 ], tumor segmentation [ 3 ], etc. With the remarkable performance of automatic medical segmentation, many practical applications have become available to achieve precise treatment and speedy disease diagnosis [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Attentional adversarial training for few-shot medical image segmentation without annotations

Awudong,

Li,

Liang

et al. 2024

PLoS ONE

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Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a scarcity of annotated labels, making it difficult to train a robust and generalized model. Few-shot learning has the potential to predict new classes that are unseen in training with a few annotations. In this study, a novel few-shot semantic segmentation framework named prototype-based generative adversarial network (PG-Net) is proposed for medical image segmentation without annotations. The proposed PG-Net consists of two subnetworks: the prototype-based segmentation network (P-Net) and the guided evaluation network (G-Net). On one hand, the P-Net as a generator focuses on extracting multi-scale features and local spatial information in order to produce refined predictions with discriminative context between foreground and background. On the other hand, the G-Net as a discriminator, which employs an attention mechanism, further distills the relation knowledge between support and query, and contributes to P-Net producing segmentation masks of query with more similar distributions as support. Hence, the PG-Net can enhance segmentation quality by an adversarial training strategy. Compared to the state-of-the-art (SOTA) few-shot segmentation methods, comparative experiments demonstrate that the proposed PG-Net provides noticeably more robust and prominent generalization ability on different medical image modality datasets, including an abdominal Computed Tomography (CT) dataset and an abdominal Magnetic Resonance Imaging (MRI) dataset.

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