> For the complete documentation index, see [llms.txt](https://www.parapathology.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.parapathology.com/computational-digital-and-mathematical-pathology/articles-on-computational-digital-and-mathematical-pathology.md). # Articles on computational, digital, and mathematical pathology #### Towards robust foundation models for digital pathology Reviewed in [Towards robust foundation models for digital pathology](/appendix/clippings/towards-robust-foundation-models-for-digital-pathology.md) — PathoROB, a public benchmark showing all 20 pathology foundation models tested encode the contributing hospital strongly enough to cause diagnostic failures (Kömen et al., Nature Communications 17, 5218, 2026), with code at [bifold-pathomics/PathoROB](https://github.com/bifold-pathomics/PathoROB). #### Confirmation bias and time pressure during human–AI collaboration in computational pathology Reviewed in [When Two Wrongs Don't Make a Right: Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology](/appendix/clippings/when-two-wrongs-dont-make-a-right-examining-confirmation-bias-and-the-role-of-time-pressure-during-h.md) — 28 pathologists estimated tumour cell percentage twice; when AI agreed with an initially wrong estimate, reliance on prior judgement collapsed (p=0.09) and AI advice dominated, while time pressure attenuated confirmation bias only because automation bias eclipsed it (Rosbach et al., CHI '25 / arXiv:2411.01007). #### Cognitive biases in AI–assisted medical decision making: a primer for pathology Reviewed in [Cognitive biases in AI-assisted medical decision making: A structured review as a primer for veterinary and human pathology](/appendix/clippings/cognitive-biases-in-ai-assisted-medical-decision-making-a-structured-review-as-a-primer-for-veterina.md) — structured review across ACM, IEEE, and PubMed identifying 12 cognitive biases in AI-assisted medicine; revealed that across all medical specialties, only one primary study came from pathology, prompting author-developed hypothetical pathology vignettes as a practical primer (Rosbach et al., Veterinary Pathology 2026, PMID 42557856). #### Screening efficiency over experience: Rapid target detection in digital cytology Reviewed in [Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology](/appendix/clippings/screening-efficiency-over-experience-rapid-target-detection-in-low-power-field-as-a-modifiable-cogni.md) — eye-tracking study of 100 cytotechnologists and 28 students demonstrating the "experience paradox": years of experience showed no significant correlation with diagnostic accuracy ($r = 0.189$), while shorter fixation on low-power targets ("pop-out" detection) was the sole independent predictor ($p = .045$), with trainees acquiring expert selective noise neglect within 3 months (Abe et al., Cancer Cytopathology 2026, DOI: 10.1002/cncy.70132). #### A distributional robustness margin for pathology foundation models Reviewed in [A distributional robustness margin for pathology foundation models](/appendix/clippings/a-distributional-robustness-margin-for-pathology-foundation-models.md) — argues PathoROB's Robustness Index is structurally unfit for cross-model comparison and replaces it with CRoMa, a per-sample signed margin (Grisi, van der Laak & Litjens, arXiv:2607.25497), with the library evaluated in [CRoMa](/computational-digital-and-mathematical-pathology/croma.md). #### Weakly supervised multiple instance learning histopathological tumor segmentation Reviewed in [Weakly supervised MIL histopathological tumor segmentation](https://github.com/sbalci/ParaPathology/tree/master/computational-digital-and-mathematical-pathology/weakly-supervised-mil-histopathological-tumor-segmentation.md) — MIL tumor segmentation from slide-level labels only (Lerousseau et al., MICCAI 2020), with code and 6,481 released TCGA tumor maps at [MarvinLer/tcga\_segmentation](https://github.com/MarvinLer/tcga_segmentation). #### Tumor budding T-cell graphs: assessing the need for resection in pT1 colorectal cancer patients Reviewed in [Tumor budding T-cell graphs for pT1 colorectal cancer](https://github.com/sbalci/ParaPathology/tree/master/computational-digital-and-mathematical-pathology/tumor-budding-t-cell-graphs-pt1-colorectal-cancer.md) — GNNs over tumor-bud/T-cell hotspot graphs raise the specificity of lymph-node-metastasis prediction by \~20 points over guideline stratification at equal sensitivity (Studer et al., MIDL 2023), with the pT1-HBTG dataset on [Zenodo](https://zenodo.org/records/7867085) and code at [digitalpathologybern/pT1-HBTG-MIDL2023](https://github.com/digitalpathologybern/pT1-HBTG-MIDL2023). #### Reporting tumor budding in colorectal cancer: ITBCC 2016 consensus recommendations Reviewed in [Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016](/appendix/clippings/recommendations-for-reporting-tumor-budding-in-colorectal-cancer-based-on-the-international-tumor-bu.md) — landmark international consensus defining tumor budding as detached single cells or clusters $\le 4$ cells, standardizing the 0.785 mm² hotspot on H\&E and introducing the 3-tier grading system (Bd1–Bd3) to guide surgical escalation in pT1 and adjuvant chemotherapy in stage II CRC (Lugli et al., Modern Pathology 2017). #### What AI Can and Cannot Do in Pathology Reviewed in [What AI Can and Cannot Do in Pathology](https://github.com/sbalci/ParaPathology/tree/master/computational-digital-and-mathematical-pathology/what-ai-can-and-cannot-do-in-pathology.md) — pathCast lecture by Dr. Rajendra Singh (UPenn; founder of PathPresenter), 2026-08-18. Argues the real risk is not replacement but **bypass** — slides shipped to commercial vendors whose predictions reach the oncologist directly — and that the answer is for departments to re-validate vendor models on their own data and own the governance layer — AI may be invisible in the workflow, but never unaudited in the record. Summarised from auto-generated captions; slides and demos not captured. #### TRICARE: Deep-learning triage of 3D pathology datasets Reviewed in [TRICARE: Deep-learning triage of 3D pathology datasets](https://github.com/sbalci/ParaPathology/tree/master/computational-digital-and-mathematical-pathology/tricare-deep-learning-triage-3d-pathology.md) — 2.5D context-aware deep learning triage for 3D open-top light-sheet microscopy, prioritizing high-risk 2D slices in prostate and Barrett's esophagus biopsies (Gao et al., Nature Biomedical Engineering 2026), with dataset at [TCIA](https://www.cancerimagingarchive.net/collection/pca_bx_3dpathology/), code at [alecgao066/TRICARE](https://github.com/alecgao066/TRICARE), and Zenodo record at [zenodo.20052262](https://doi.org/10.5281/zenodo.20052262). #### HistoPLUS: Comprehensive cellular characterisation of H\&E slides Reviewed in [HistoPLUS: Towards Comprehensive Cellular Characterisation of H\&E Slides](/appendix/clippings/histoplus-towards-comprehensive-cellular-characterisation-of-h-and-e-slides.md) — cell detection, boundary segmentation, and 13-class classification framework integrating the compact distilled Bioptimus H0-mini pathology foundation model (86M params) within a 3-branch CellViT architecture. Trained via active learning on HistoTRAIN (108,722 nuclei, 6 indications) with [NuClick](/computational-digital-and-mathematical-pathology/digital-pathology-software/nuclick.md) boundary propagation and validated across 6 MOSAIC cohorts (HistoVAL: 69,108 consensus nuclei from multi-pathologist review). Outperforms current state-of-the-art models by +5.2% in detection quality and +23.7% in classification F1 while matching ViT-Huge models with 5× fewer parameters, unlocking 7 understudied cell populations, predicting *FGFR3* mutation status in bladder cancer, and generalizing zero-shot to unseen breast and ovarian cancers (Adjadj et al., Journal of Pathology Informatics 2026, DOI: 10.1016/j.jpi.2026.100696, PII [S2153-3539(26)00156-2](https://www.sciencedirect.com/science/article/pii/S2153353926001562); arXiv:2508.09926). Code at [owkin/histoplus](https://github.com/owkin/histoplus), weights on [Hugging Face](https://huggingface.co/Owkin-Bioptimus/histoplus). #### CytoFormer: Molecularly supervised cell foundation model for histopathology cell classification Reviewed in [CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification](/appendix/clippings/cytoformer-a-molecularly-supervised-cell-foundation-model-for-histopathology-cell-classification.md) — replaces manual pathologist cell annotation with molecular ground truth derived from paired in situ spatial transcriptomics (81 Xenium sections, 15.4M cells, 16 organs, 23 cell types). Leverages a ViT-giant backbone with 16 per-organ linear routing heads (84.6% accuracy, 0.78 macro-F1), outperforming 6 pathology foundation models on public benchmarks (PanNuke, CoNSeP, MoNuSAC, PUMA), transferring zero-shot to unseen organs, and achieving label-efficient active learning (F1 0.82) on TissueLab (Yao, Li, Yu & Huang, Precision Pathology 2026, DOI: 10.1016/j.prpath.2026.100006). Code at [zhihuanglab/CytoFormer](https://github.com/zhihuanglab/CytoFormer), weights on [Hugging Face](https://huggingface.co/zhihuanglab/CytoFormer), and interactive WSI viewer at [zhihuanglab.github.io/CytoFormer](https://zhihuanglab.github.io/CytoFormer/). #### ConvMixerSSM: Hybrid convolution and state-space model for WSI cancer subtyping Reviewed in [A Hybrid MIL Approach Leveraging Convolution and State-Space Model for Whole-Slide Image Cancer Subtyping](/appendix/clippings/a-hybrid-mil-approach-leveraging-convolution-and-state-space-model-for-whole-slide-image-cancer-subt.md) — hybrid weakly supervised MIL architecture combining depthwise separable convolutions (ConvMixer) for local tissue textures with linear state-space sequence modeling (Mamba/SSM) and ReLU-gated attention for sparse instance selection; sets top performance on TCGA-NSCLC (AUC 97.83%, ACC 91.82%, F1 91.18%) and CAMELYON16 (AUC 98.95%) with 1.65 ms inference latency per gigapixel slide (Bi & Zhang, Mathematics 2025, DOI: 10.3390/math13132178). #### From Samples to Knowledge 2025: QuPath Training Course Reviewed in [From Samples to Knowledge 2025: QuPath Training Course](/appendix/clippings/from-samples-to-knowledge-2025-qupath-training-course.md) — 14-session practical curriculum from the La Jolla Institute for Immunology (Mikulski & McArdle) establishing QuPath v0.6.0+ best practices for 18-plex RareCyte Orion immunofluorescence, deep learning boundary segmentation (InstanSeg / DJL), headless Groovy batch scripting, composite object phenotyping, multimodal H\&E registration (Warpy), spatial proximity transforms, and Paquo/QuBylab Python connectivity. #### The benefits of building and working with interactive simulations Interactive simulations for better model intuition [**Paradoxical Dependencies of Tumor Dormancy and Progression on Basic Cell Kinetics**. Heiko Enderling, Alexander R.A. Anderson, Mark A.J. Chaplain, Afshin Beheshti, Lynn Hlatky and Philip Hahnfeldt. Cancer Res November 15 2009 (69) (22) 8814-8821; DOI: 10.1158/0008-5472.CAN-09-2115](https://cancerres.aacrjournals.org/content/69/22/8814.long) #### Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis ## Computational, Digital & Mathematical Pathology * [CompPath Lecture Series 2016-2017 Semester](https://www.youtube.com/playlist?list=PLwdWByS9hJ3IpNyN1Ge4dbfSV43296jLn) CompPath Dr. Shikhar Uttam, Ph.D. Lecture (April 14th 2017) * [Foundations of Computational and Systems Biology](https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014) 1. Introduction to Computational and Systems Biology 2. Local Alignment (BLAST) and Statistics * Biologists would love to program cells as if they were computer chips * Using the principles of evolution to treat and prevent cancer