AI in cancer detection

Latest papers

• AI-assisted mitotic counting improves reproducibility and efficiency across multiple tumour types

  Simon Graham, Mostafa Jahanifar, Quoc Dang Vu, Vygante Maskoliunaite, Donatas Petroska, Ruta Barbora Valkiuniene, Ayat Gamal Lashen, Jen Hong Ong, Amede Ogechi Nnorom, Sinclair Couper, Natasha Kardasz, Reshma Agrawal, Brinder Singh Chohan, Jose Luis Solorzano Rendon, Shonali Natu, Arvydas Laurinavicius, Nasir Rajpoot, David Snead · 2 October 2026

  Mitotic counting is an important component of tumour grading, diagnosis and prognostic assessment across several tumour types, but manual assessment is time-consuming and subject to inter-pathologist variability. To help address these challenges, we developed MitPro, an AI tool designed to improve c…

• SCALE: Synthetic Calibration via Agreement Labeling in Embedding Space

  Wenjun Liu, Saeed Hassanpour · 1 October 2026

  Foundation models for computational pathology are usually evaluated using AUC and accuracy, while calibration is often left untested. This matters because a model can be accurate on average but still assign overly confident probabilities to cases that are difficult even for pathologists. We study ca…

• Colorectal Cancer Segmentation with Adaptive Augmentation and Multi-Resolution Ensemble Models

  \"Umit Mert \c{C}a\u{g}lar, Alptekin Temizel · 1 October 2026

  Colorectal cancer (CRC) is the second most deadly and third most common cancer, and the leading cause of death among gastrointestinal cancers. Early diagnosis is crucial for the treatment of this cancer and increasing the survival rates. Although CRC is more common in developed regions, its occurren…

• HERO: Histology Encoder for Robust Representation in Oncology

  Zhi Li (Caris Life Sciences, Irving, TX, United States), Eghbal Amidi (Caris Life Sciences, Irving, TX, United States), Yating Cheng (Caris Life Sciences, Irving, TX, United States), Tyson Dawson (Caris Life Sciences, Irving, TX, United States), Gorkem Can Ates (Caris Life Sciences, Irving, TX, United States), Shuzhen Kuang (Caris Life Sciences, Irving, TX, United States), Norsang Lama (Caris Life Sciences, Irving, TX, United States), Md Ashequr Rahman (Caris Life Sciences, Irving, TX, United States), Zhiying Lu (Caris Life Sciences, Irving, TX, United States), Elisabeth K. Kong (Caris Life Sciences, Irving, TX, United States), Milan Radovich (Caris Life Sciences, Irving, TX, United States), David Spetzler (Caris Life Sciences, Irving, TX, United States), Matthew Oberley (Caris Life Sciences, Irving, TX, United States), George W. Sledge (Caris Life Sciences, Irving, TX, United States), Ming Chen (Caris Life Sciences, Irving, TX, United States) · 30 September 2026

  Foundation models trained on large pathology image corpora now provide strong, transferable representations for computational pathology. Over the past few years a series of such models has been released, each trained on more slides than the last; on standard classification and segmentation benchmark…

• See, Measure, and Reason: Learning Visually Grounded Reasoning in Pathology

  Chengyang Zhang, Wenchuan Zhang, Bo Li, Mengran Li, Xinyu Liu, Jiaming Yang, Jie Chen, Zhang Zhang, Yuhao Yi, Hong Bu, Jiancheng Lv · 29 September 2026

  Pathological assessment relies on recognizing fine-grained visual details in histological images. Vision-language models (VLMs) increasingly support pathology interpretation, yet their ability to perceive these details remains inadequate. This weakness leads to inaccurate cellular observations that …

• Multimodal LLMs Outperform Pathology Foundation Models in Cross-Domain Histological Similarity

  Yishu Zhang, Yun Li, Daiwei Zhang · 29 September 2026

  State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons cross slide or institution boundaries. We show that general-purpose multimodal LLMs, without being trained as pathology foundation models, consistently outper…

• MoSPR: Histology-to-Gene Expression Prediction with Morpho-Spatial Macrostates and Low-Rank Molecular Programs

  Dongmyung Shin, Geongyu Lee, Yesung Cho, Park Jong Bae · 29 September 2026

  Predicting molecular profiles from histopathology remains challenging because whole-slide images contain spatially organized, heterogeneous tissue patterns, while gene expression comprises thousands of correlated targets. We introduce MoSPR (Morpho-Spatial Program Regression), a linear framework tha…

• CytoSPM: Open-Vocabulary Cytopathology Detection with Structured Prompt Bank

  Wenjie Li, Zishan Xu, Jinyang Huang, Zhengxin Nie, Shichao Kan, Yixiong Liang · 28 September 2026

  Cytopathology detection requires open-vocabulary recognition because cellular categories are fine-grained, long-tailed, and continuously evolving across different organ systems. However, existing cytology detectors are mostly single-domain and closed-set, and there is still no unified benchmark for …

• Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images

  Tiffanie Godelaine, Manon Dausort, Karim El Khoury, Beno\^it G\'erin, Beno\^it Macq, Christophe De Vleeschouwer · 28 September 2026

  Automating the analysis of whole-slide images (WSIs), a key step in cancer diagnosis, has high clinical value, as it can reduce pathologist's workload while improving diagnosis accuracy. Recently, vision-language models have shown promising performance for patch-level classification without requirin…

• Refining Cytology Predictions with Conditional Random Fields

  Manon Dausort, Tiffanie Godelaine, Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer, Beno\^it Macq · 28 September 2026

  Vision-language models (VLMs) achieve strong zero-shot (ZS) classification on histology images but do not perform as well on cytology, whose stains and cell morphology differ markedly compared to histology. Conditional random fields (CRFs) can refine noisy VLM predictions by propagating information …

• FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification

  Muhammad Muhtasim Shahriar, M. M. Golam Hafiz, Saad Aloteibi, Mohammad Ali Moni · 28 September 2026

  Cross-site lung histopathology classification must account for stain variation, non-IID client data, missing classes, and the cost of adapting large pathology encoders. This study evaluates FedHisto-PAST v2 for three-way classification of adenocarcinoma (ACA), Normal, and squamous cell carcinoma (SC…

• Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification

  Il\'an Carretero, Pablo Meseguer, Roc\'io del Amor, Valery Naranjo · 24 September 2026

  Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PFMs provide rich, discriminative representations for whole slide image (WSI) analysis, enabling tasks such as slide-level classification under multiple…

• FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology

  Anh-Tien Nguyen, Trung DQ. Dang, Nghiem Tuong Diep, Bui Ngoc Han Nguyen, Tan-Ha Mai, Miriam Cindy Maurer, Phuong Hoa Nguyen, Thi Thuy Uyen Nguyen, Youngjun Park, Daniel Sonntag, Duy Minh Ho Nguyen, Anne-Christin Hauschild · 24 September 2026

  Pathology vision-language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high cost of expert pathology annotation can also limit the labeled data available for task-specific adaptation. Combining complementary pretrained repres…

• WILSON - a pathology foundation model framework for patient-level analysis and diagnostic text generation

  Saghir Alfasly, Wataru Uegami, Sobhan Hemati, Wenchao Han, Xiaojia Tang, Kevin Thompson, Daniel Stone, Ghazal Alabtah, Saba Yasir, Michael R. Lucas, Eric W. Klee, Cheryl L. Willman, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H. R. Tizhoosh · 23 September 2026

  Pathologists integrate morphology across magnifications and across the slides of a patient case, whereas pathology foundation models encode thousands of tiles from single slides and aggregate their features. Here we present WILSON, a vision--language foundation model that represents whole-slide imag…

• MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis

  Trinh T. L. Vuong, Simon Graham, Quoc Dang Vu, Phat T. H. Ho, Jeewoo Lim, Mostafa Jahanifar, Nasir Rajpoot, Jin T. Kwak · 22 September 2026

  Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type and image acquisition. We present MiTHras, a task-specific pretraining framework that combines pseudo-label-guided image- and token-level contrastive lea…

• Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Images

  Hyeseong Lee, Eunsu Kim, D M Bappy, Ho Heon Kim, Youngsuk Lee, Se Young Chun, Jang-Hwan Choi, Sung Hak Lee, Sangjeong Ahn · 22 September 2026

  Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing essential morphology. Current pathology image restoration methods are mostly restricted to small patches, struggling to maintain global structural c…

• SLICEChat: Progressive In-Encoder Token Pruning for Whole-Slide Pathology Language Models

  Ali Kerem Bozkurt, Baris Cem Bakay, Ibrahim Kulac, Cigdem Gunduz-Demir, Erkut Erdem, Aykut Erdem · 22 September 2026

  Whole-slide pathology images (WSIs) contain gigapixel-scale visual content, creating a major scalability challenge for slide-level multimodal large language models (MLLMs). Existing approaches process thousands of patch tokens and typically apply compression only after slide encoding, leaving multim…

• MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention

  Muhammet Sami Yavuz, Sabri Mustafa Kahya, Richard R. Chen, Jana Lipkova, Benedikt Wiestler · 21 September 2026

  Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity. To address these challenges, we propose MIST, multimodal survival prediction wit…

• Performance of Machine Learning Classification in Sonomammogram Images using BI-RADS

  Malitha Gunawardhana, Norbert Zolek · 18 September 2026

  This research aims to investigate the classification accuracy of various state-of-the-art image classification models across different categories of breast ultrasound images, as defined by the Breast Imaging Reporting and Data System (BI-RADS). To achieve this, we used 2,945 sonomammogram images for…

• Seeing Abnormal from Normal: Glomerular Abnormality in Representations of Normal Renal Morphology

  Greta Hasko, Rachit Saluja, Tianyu Shi, Leiyue Zhao, Yuechen Yang, Daniel Reisenbuechler, Tianyuan Yao, Zhenhao Guo, John Cannon, Haichun Yang, Yuankai Huo, Yuling Chi, Lorraine Gudas, Mert R. Sabuncu, Yihe Yang, Ruining Deng · 18 September 2026

  Fine-grained evaluation of glomerular pathology must distinguish normal glomeruli from abnormalities such as global and segmental glomerulosclerosis, obsolescent, ischemic, solidified, disappearing, and atubular glomeruli. Supervised classification requires labeled examples of every category, which …

• Conditioning noise is a free regularizer for LoRA fine-tuning: no pathology encoder required for diffusion-based artifact detection in histopathology

  Konstantinos Moutselos, Ilias Maglogiannis · 16 September 2026

  Diffusion-based artifact detectors score whole-slide image patches by reconstruction error under a model fine-tuned on clean tissue. We show that conditioning this fine-tuning on random Gaussian embeddings -- resampled at every step from approx. 200 KB of precomputed embedding statistics, with no en…

• Sharing standardized image-derived data in computational pathology using DICOM

  Daniela P. Schacherer (Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany), Christopher P. Bridge (Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA), David Clunie (PixelMed Publishing, Bangor, USA), Igor Octaviano (Radical Imaging LLC, USA), Andr\'e Homeyer (Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany), Markus D. Herrmann (Harvard Medical School, Boston, USA), Olivier Gevaert (Stanford University, Stanford, USA), Tabita Ghete (University Hospital Erlangen, Erlangen, Germany), Markus Metzler (University Hospital Erlangen, Erlangen, Germany), Henning Hoefener (Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany), Tahsin Kurc (Stony Brook University, Stony Brook, USA), Curtis Lisle (National Institute of Allergy and Infectious Diseases, Bethesda, USA), Kenneth Philbrick (Google Research, Mountain View, USA), Joel Saltz (Stony Brook University, Stony Brook, USA), Yuanning Zheng (Stanford University, Stanford, USA), Andrey Fedorov (Harvard Medical School, Boston, USA) · 15 September 2026

  Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into collecting, centralizing, and sharing pathology imaging data. In contrast, sharing of image-derived data such as region-of…

• ProtoCAM: Interpretable Few-Shot Mask-Guided Prototypical Learning for Breast Lesion Classification in Ultrasound Imaging

  Ashkan Ebadi · 15 September 2026

  Breast ultrasound imaging plays an important role in the early detection and diagnosis of breast cancer, particularly for patients with dense breast tissue. However, developing reliable deep learning models for ultrasound analysis is challenging due to limited annotated medical data and the need for…

• Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images

  Juzheng Miao, Yuchen Yuan, Cheng Chen, Pheng-Ann Heng · 14 September 2026

  Vision-Language Models such as CLIP enable effective few-shot medical anomaly detection (AD) via strong image-text semantic alignment. However, their globally contrastive pretraining lacks explicit spatial supervision, limiting precise lesion localization. In contrast, Vision Foundation Models (VFMs…

• Longitudinal Risk Prediction in Mammography with Privileged History Distillation

  Banafsheh Karimian, Soufiane Belharbi, Alexis Guichemerre, Luke McCaffrey, Mohammadhadi Shateri, Eric Granger · 11 September 2026

  Longitudinal mammography screening has become an important source of information for improving future breast cancer risk prediction. However, the performance of current longitudinal mammography models degrades when prior examinations are unavailable at inference, creating a structured privileged-inf…