标题： MyGO：使您的目标显而易见，在前列腺癌病灶区域分割中避免语义混淆 显示英文标题

标题： MyGO: Make your Goals Obvious, Avoiding Semantic Confusion in Prostate Cancer Lesion Region Segmentation

摘要： 早期诊断和准确识别前列腺癌（PCa）中的病灶位置和进展对于帮助临床医生制定有效的治疗策略至关重要。 然而，由于病灶区域与非病灶区域之间存在高度的语义同质性，现有的医学图像分割方法往往难以准确理解病灶语义，导致语义混淆的问题。 为了解决这一挑战，我们提出了一种新的像素锚点模块，该模块引导模型发现一组稀疏的特征锚点，用于捕捉和解释全局上下文信息。 此机制增强了模型的非线性表示能力，并提高了病灶区域内的分割准确性。 此外，我们设计了一种基于自注意力的Top_k选择策略，以进一步精确定位这些特征锚点，并引入焦点损失函数以缓解类别不平衡问题，从而促进在不同区域内的更精确语义解释。 我们的方法在PI-CAI数据集上实现了最先进的性能，展示了69.73%的IoU和74.32%的Dice分数，并显著提高了前列腺癌病灶检测效果。 显示英文摘要

摘要： Early diagnosis and accurate identification of lesion location and progression in prostate cancer (PCa) are critical for assisting clinicians in formulating effective treatment strategies. However, due to the high semantic homogeneity between lesion and non-lesion areas, existing medical image segmentation methods often struggle to accurately comprehend lesion semantics, resulting in the problem of semantic confusion. To address this challenge, we propose a novel Pixel Anchor Module, which guides the model to discover a sparse set of feature anchors that serve to capture and interpret global contextual information. This mechanism enhances the model's nonlinear representation capacity and improves segmentation accuracy within lesion regions. Moreover, we design a self-attention-based Top_k selection strategy to further refine the identification of these feature anchors, and incorporate a focal loss function to mitigate class imbalance, thereby facilitating more precise semantic interpretation across diverse regions. Our method achieves state-of-the-art performance on the PI-CAI dataset, demonstrating 69.73% IoU and 74.32% Dice scores, and significantly improving prostate cancer lesion detection.