MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction

• URL: http://arxiv.org/abs/2604.06390v2
• Date: Mon, 13 Apr 2026 21:39:29 GMT
• Title: MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
• Abstract summary: We propose MorphDistill, a framework that distills complementary knowledge from multiple pathology foundation models into a CRC-specific encoder.<n>In Stage I, a student encoder is trained using dimension-agnostic multi-teacher distillation with supervised contrastive regularization on large-scale colorectal datasets.<n>In Stage II, the encoder extracts patch-level features from whole-slide images, which are aggregated via attention-based multiple instance learning to predict five-year survival.
• Score: 3.2844015242965927
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treatment stratification, yet existing pathology foundation models often overlook organ-specific features critical for CRC prognostication. Methods: We propose MorphDistill, a two-stage framework that distills complementary knowledge from multiple pathology foundation models into a compact CRC-specific encoder. In Stage I, a student encoder is trained using dimension-agnostic multi-teacher relational distillation with supervised contrastive regularization on large-scale colorectal datasets. This preserves inter-sample relationships from ten foundation models without explicit feature alignment. In Stage II, the encoder extracts patch-level features from whole-slide images, which are aggregated via attention-based multiple instance learning to predict five-year survival. Results: On the Alliance/CALGB 89803 cohort (n=424, stage III CRC), MorphDistill achieves an AUC of 0.68 (SD 0.08), an approximately 8% relative improvement over the strongest baseline (AUC 0.63). It also attains a C-index of 0.661 and a hazard ratio of 2.52 (95% CI: 1.73-3.65), outperforming all baselines. On an external TCGA cohort (n=562), it achieves a C-index of 0.628, demonstrating strong generalization across datasets and robustness across clinical subgroups. Conclusion: MorphDistill enables task-specific representation learning by integrating knowledge from multiple foundation models into a unified encoder. This approach provides an efficient strategy for prognostic modeling in computational pathology, with potential for broader oncology applications. Further validation across additional cohorts and disease stages is warranted.

Related papers

• CorePath: A Breast-Specialized Pathology Foundation Model for Core Needle Biopsy Diagnosis and Risk-Controlled Report Generation [19.43990244213587]
  CorePath is a breast-specialized multimodal pathology foundation model fine-tuned from PRISM.<n>CorePath consistently outperformed PRISM across cancer detection, invasion assessment, and histological subtyping.
  arXiv  Detail & Related papers  (2026-08-04T03:43:49Z)
• PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities [86.63247982275396]
  PRIME is a missing-aware multimodal self-supervised pretraining framework.<n>It learns robust and transferable representations from partially observed cohorts.<n>We evaluate PRIME on The Cancer Genome Atlas with label-free pretraining on 32 cancer types.
  arXiv  Detail & Related papers  (2026-04-05T21:14:27Z)
• A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection [0.2958068627971103]
  The PAM50 gene signature is widely recognized as a standard for classifying breast cancer into intrinsic subtypes.<n>In this study, we introduce a novel optimization-driven deep learning framework that aims to reduce reliance on costly molecular assays.<n>Our method jointly optimize patch informativeness, spatial diversity, uncertainty, and patch count by combining the non-dominated sorting genetic algorithm II (NSGA-II) with Monte Carlo dropout-based uncertainty estimation.
  arXiv  Detail & Related papers  (2026-04-02T09:13:24Z)
• Residual GRU+MHSA: A Lightweight Hybrid Recurrent Attention Model for Cardiovascular Disease Detection [1.267904597444312]
  We propose Residual GRU with Multi-Head Self-Attention, a compact deep learning architecture for clinical records.<n>We evaluate the model on the UCI Heart Disease dataset using 5-fold stratified cross-validation.<n>The proposed model achieves an accuracy of 0.861, macro-F1 of 0.860, ROC-AUC of 0.908, and PR-AUC of 0.904, outperforming all baselines.
  arXiv  Detail & Related papers  (2025-12-16T16:33:59Z)
• MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis [40.3028468133626]
  Multiple instance learning (MIL) has emerged as the dominant paradigm for whole slide image (WSI) analysis in computational pathology.<n>textbfMeCaMIL, a causality-aware MIL framework, explicitly models demographic confounders through structured causal graphs.<n>MeCaMIL achieves superior fairness -- demographic disparity variance drops by over 65% relative reduction on average across attributes.
  arXiv  Detail & Related papers  (2025-11-14T06:47:21Z)
• Cancer-Net PCa-MultiSeg: Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging [55.62977326180104]
  Current deep learning approaches for prostate cancer lesion segmentation achieve limited performance.<n>We investigate synthetic correlated diffusion imaging (CDI$s$) as an enhancement to standard diffusion-based protocols.<n>Our results establish validated integration pathways for CDI$s$ as a practical drop-in enhancement for PCa lesion segmentation tasks.
  arXiv  Detail & Related papers  (2025-11-11T04:16:12Z)
• Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning [0.0]
  We propose a scalable, privacy-preserving federated learning framework for colorectal cancer grading.<n>Our framework achieves an overall accuracy of 83.5%, outperforming a comparable centralized model.<n>The proposed modular pipeline establishes a foundational step toward deployable, privacy-aware clinical AI for digital pathology.
  arXiv  Detail & Related papers  (2025-11-05T18:18:09Z)
• A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer [54.58205672910646]
  RenalCLIP is a visual-language foundation model for characterization, diagnosis and prognosis of renal mass.<n>It achieved better performance and superior generalizability across 10 core tasks spanning the full clinical workflow of kidney cancer.
  arXiv  Detail & Related papers  (2025-08-22T17:48:19Z)
• A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis [18.549808005574985]
  Multi-OSCC is a new histopathology image dataset comprising 1,325 Oral Squamous Cell Carcinoma patients.<n>Each patient is represented by six high resolution histopathology images captured at x200, x400, and x1000 magnifications-two per magnification-covering both the core and edge tumor regions.<n>The dataset is richly annotated for six critical clinical tasks: recurrence prediction (REC), lymph node metastasis (LNM), tumor differentiation (TD), tumor invasion (TI) and perineural invasion (PI)
  arXiv  Detail & Related papers  (2025-07-22T08:48:45Z)
• PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks [39.97710183184273]
  We present PathOrchestra, a versatile pathology foundation model trained via self-supervised learning on a dataset comprising 300K pathological slides.<n>The model was rigorously evaluated on 112 clinical tasks using a combination of 61 private and 51 public datasets.<n>PathOrchestra demonstrated exceptional performance across 27,755 WSIs and 9,415,729 ROIs, achieving over 0.950 accuracy in 47 tasks.
  arXiv  Detail & Related papers  (2025-03-31T17:28:02Z)
• A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction [6.199310532720352]
  We developed a multi-modal ensemble model (MMEM) that integrates clinical data, multi-omics data, and histopathology whole slide image (WSI) data.<n>MMEM predicted overall survival (OS) and disease-free survival (DFS) for ccRCC patients.
  arXiv  Detail & Related papers  (2024-12-10T02:51:14Z)
• CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images [42.771819949806655]
  We introduce CIMIL-CRC', a framework that solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort.
  arXiv  Detail & Related papers  (2024-01-29T12:56:11Z)

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.