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

• URL: http://arxiv.org/abs/2507.17269v1
• Date: Wed, 23 Jul 2025 07:10:07 GMT
• Title: MyGO: Make your Goals Obvious, Avoiding Semantic Confusion in Prostate Cancer Lesion Region Segmentation
• Abstract summary: We propose a novel Pixel Anchor Module, which guides the model to discover a sparse set of feature anchors.<n>This mechanism enhances the model's nonlinear representation capacity and improves segmentation accuracy within lesion regions.<n>Our method achieves state-of-the-art performance on the PI-CAI dataset, demonstrating 69.73% IoU and 74.32% Dice scores.
• Score: 14.346163388200148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography [0.0]
  BiLoG-Net is a deep learning framework that jointly performs breast mass segmentation and malignancy classification.<n>Our architecture integrates a novel encoder-decoder paradigm with Fire-based feature extraction, lightweight global and local feature enhancement modules, and adaptive location-aware gating.<n>BiLoG-Net achieves state-of-the-art performance with Dice scores of 94.20% and 93.10%, classification accuracies of 95.20% and 93.60%, and AUC values of 97.10% and 96.00%, respectively.
  arXiv  Detail & Related papers  (2026-07-11T08:04:35Z)
• An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis [1.2871968485402088]
  Existing vision-language models fail to address the extreme class imbalance prevalent in clinical segmentation datasets.<n>We present an end-to-end Explainable Vision-Language Model framework designed to overcome these limitations.<n>We show considerable performance: a diagnostic classification accuracy of 90.69%, a macro-averaged Dice score of 0.9512 for segmentation, and a CIDEr score of 92.80%.
  arXiv  Detail & Related papers  (2026-04-02T20:18:35Z)
• Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation [2.056389633034677]
  This work aims to bridge the gap between automated and manual segmentation through a framework driven by user-provided point prompts.<n>The framework was evaluated on two public prostate MR datasets (PROMIS and PICAI, with 566 and 1090 cases)
  arXiv  Detail & Related papers  (2026-02-19T20:29:41Z)
• Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment [42.09313885494969]
  We harness the Swin Transformer's capacity to discern extended spatial dependencies within images through the hierarchical framework. Our novel contribution lies in refining local feature representations, orienting them specifically toward the final distribution of the classifier. Our model demonstrates significant robustness and precision, as evidenced by extensive validation of two established benchmarks for Knee OsteoArthritis (KOA) grade classification.
  arXiv  Detail & Related papers  (2024-03-15T01:09:58Z)
• Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations [49.33388736227072]
  We propose a semi- and weakly-supervised learning framework for mass segmentation. We use limited strongly-labeled samples and sufficient weakly-labeled samples to achieve satisfactory performance. Experiments on CBIS-DDSM and INbreast datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-03-14T12:05:25Z)
• GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation [41.97669338211682]
  We propose a novel domain generalization strategy that employs gradient surgery exponential moving average (GS-EMA) optimization technique and boundary-aware contrastive learning (BACL) Our approach is distinct in its ability to adapt to new, unseen domains by learning domain-invariant features, thereby improving the robustness and accuracy of aneurysm segmentation across diverse clinical datasets.
  arXiv  Detail & Related papers  (2024-02-23T10:02:15Z)
• Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning [46.248404274124546]
  Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. This paper proposes a simple yet potent contrastive learning framework for UDA to narrow the inter-domain gap between labelled source and unlabelled target distribution.
  arXiv  Detail & Related papers  (2024-02-23T10:01:22Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach [1.180462901068842]
  Focal cortical dysplasia (FCD) is a leading cause of drug-resistant epilepsy, which can be cured by surgery. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. We propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions.
  arXiv  Detail & Related papers  (2023-06-02T08:56:56Z)
• Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation [8.668715385199889]
  We present a few-shot segmentation approach for ischemic lesion segmentation using only one annotated sample during training. We exploit color-coded parametric maps generated from Computed Tomography Perfusion scans. Given a single annotated patient, an average Dice score of 0.58 is achieved for the segmentation of ischemic lesions.
  arXiv  Detail & Related papers  (2023-03-02T15:10:08Z)
• Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT [41.199716328468895]
  We develop a dual-path transformer to exploit the feasibility of classification and segmentation of pancreatic lesions. The proposed method consists of a CNN-based segmentation path (S-path) and a transformer-based classification path (C-path) Our results show that our method can enable accurate classification and segmentation of the full taxonomy of pancreatic lesions.
  arXiv  Detail & Related papers  (2023-03-02T03:34:28Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation [3.1671604920729224]
  Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain only image-level labels rather than pixel-based annotations. This paper leverages self-supervised equivariant learning and attention-based multi-instance learning to tackle this problem. We conduct extensive validation experiments on the Eyepacs dataset, achieving an area under the receiver operating characteristic curve (AU ROC) of 0.958, outperforming current state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-10-12T06:26:05Z)
• Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss [16.80758525711538]
  We present a novel weakly-supervised universal lesion segmentation method based on the High-Resolution Network (HRNet) AHRNet provides advanced high-resolution deep image features by involving a decoder, dual-attention and scale attention mechanisms. Our method achieves the best performance on the publicly large-scale DeepLesion dataset and a hold-out test set.
  arXiv  Detail & Related papers  (2021-05-03T23:33:37Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation [18.58056402884405]
  Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. Current practices rely on an imprecise substitute called response evaluation criteria in solid tumors. This paper proposes a convolutional neural network based weakly-supervised lesion segmentation method.
  arXiv  Detail & Related papers  (2020-01-23T15:15:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.