Multi-Task Learning Network for Medical Image Analysis Guided by Lesion Regions and Spatial Relationships of Tissues
Bibliographic record
Abstract
Medical image analysis plays key role in computer-aided diagnosis, where segmentation and classification are essential and interconnected tasks. While multi-task learning (MTL) has been widely explored to leverage inter-task synergies, effectively guiding knowledge transfer to prevent task conflict and negative transfer remains a key challenge, particularly in anatomically complex diagnostic scenarios. This paper presents LTRMTL-Net, a novel multi-task learning framework for medical image analysis that simultaneously addresses segmentation and classification tasks guided by lesion regions and spatial relationships of tissues. The proposed architecture integrates an Enhanced Lesion Region Fusion (ELRF) module that leverages GradCAM-guided attention mechanisms to precisely locate and enhance lesion regions, providing critical prior knowledge for both tasks. Tissue Space Structure Prediction (TSSP) component captures local-global spatial dependencies through contrastive learning, establishing effective anatomical context modeling. The core encoder employs Hybrid Wavelet-State Attention blocks that combine modulated wavelet transform convolutions with structured state space models to extract multi-scale features while maintaining computational efficiency. Dual-stream inputs with symmetric architecture accommodate single-source scenarios across diverse medical imaging applications. Experimental results on mammography and breast ultrasound datasets demonstrate that the proposed method captures fine-grained lesion boundary details while providing accurate malignancy classification. Harnessing cooperative knowledge transfer between segmentation and classification, guided by anatomical priors, boosts diagnostic performance and provides comprehensive, interpretable clinical insights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".