# Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels

arXiv: https://arxiv.org/abs/2006.03394 · HTML: https://mlsanity.com/paper/2006.03394/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer](https://mlsanity.com/paper/2107.09405/) (2021)
- [Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery](https://mlsanity.com/paper/2312.02481/) (2023)
- [Resolution-Based Distillation for Efficient Histology Image Classification](https://mlsanity.com/paper/2101.04170/) (2021)
- [End-to-end Multiple Instance Learning with Gradient Accumulation](https://mlsanity.com/paper/2203.03981/) (2022)
- [Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images](https://mlsanity.com/paper/2312.12619/) (2023)
- [Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review](https://mlsanity.com/paper/2211.14847/) (2022)
- [Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling](https://mlsanity.com/paper/2403.04865/) (2024)
- [Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting](https://mlsanity.com/paper/2511.19953/) (2025)
- [Whole Slide Concepts: A Supervised Foundation Model For Pathological Images](https://mlsanity.com/paper/2507.05742/) (2025)
- [Weakly-supervised Generative Adversarial Networks for medical image classification](https://mlsanity.com/paper/2111.14605/) (2021)
- [Preparing data for pathological artificial intelligence with clinical-grade performance](https://mlsanity.com/paper/2205.10748/) (2022)
- [Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification](https://mlsanity.com/paper/2208.03526/) (2022)
- [Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines](https://mlsanity.com/paper/2302.10406/) (2023)
- [DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture](https://mlsanity.com/paper/2305.01968/) (2023)
- [Development and prospective validation of a prostate cancer detection, grading, and workflow optimization system at an academic medical center](https://mlsanity.com/paper/2410.23642/) (2024)
- [Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models](https://mlsanity.com/paper/2505.08590/) (2025)
- [TVT-PAPD: Pathology-Aware Prototype Distillation for Self-Supervised Whole Slide Image Classification](https://mlsanity.com/paper/2607.10406/) (2026)
- [Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations](https://mlsanity.com/paper/2608.28820/) (2026)
- [Sharing standardized image-derived data in computational pathology using DICOM](https://mlsanity.com/paper/2609.14530/) (2026)

## Abstract (arXiv)

Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially assist pathologists deep-learning-based cancer detection systems have been developed. Many of the state-of-the-art models are patch-based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. However, such annotations are seldom readily available, in contrast to the clinical reports of pathologists, which contain slide-level labels. As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field. In this paper, we propose to use a streaming implementation of convolutional layers, to train a modern CNN (ResNet-34) with 21 million parameters end-to-end on 4712 prostate biopsies. The method enables the use of entire biopsy images at high-resolution directly by reducing the GPU memory requirements by 2.4 TB. We show that modern CNNs, trained using our streaming approach, can extract meaningful features from high-resolution images without additional heuristics, reaching similar performance as state-of-the-art patch-based and multiple-instance learning methods. By circumventing the need for manual annotations, this approach can function as a blueprint for other tasks in histopathological diagnosis. The source code to reproduce the streaming models is available at https://github.com/DIAGNijmegen/pathology-streaming-pipeline .