# Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection

arXiv: https://arxiv.org/abs/2105.10490 · HTML: https://mlsanity.com/paper/2105.10490/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)

## Built on by (most cited first, 30 of 68)

- [Quilt-1M: One Million Image-Text Pairs for Histopathology](https://mlsanity.com/paper/2306.11207/) (2023)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology](https://mlsanity.com/paper/2401.16355/) (2024)
- [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://mlsanity.com/paper/2107.00272/) (2021)
- [CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology](https://mlsanity.com/paper/2412.12077/) (2024)
- [PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration](https://mlsanity.com/paper/2407.00203/) (2024)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Few-shot Adaptation of Medical Vision-Language Models](https://mlsanity.com/paper/2409.03868/) (2024)
- [HistoCartography: A Toolkit for Graph Analytics in Digital Pathology](https://mlsanity.com/paper/2107.10073/) (2021)
- [Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation](https://mlsanity.com/paper/2307.11397/) (2023)
- [PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning](https://mlsanity.com/paper/2408.16769/) (2024)
- [Knowledge-enhanced Visual-Language Pretraining for Computational Pathology](https://mlsanity.com/paper/2404.09942/) (2024)
- [A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model](https://mlsanity.com/paper/2507.17303/) (2025)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [Multi-Modal Foundation Models for Computational Pathology: A Survey](https://mlsanity.com/paper/2503.09091/) (2025)
- [Full Conformal Adaptation of Medical Vision-Language Models](https://mlsanity.com/paper/2506.06076/) (2025)
- [HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis](https://mlsanity.com/paper/2508.11181/) (2025)
- [Multiple Instance Learning with Mixed Supervision in Gleason Grading](https://mlsanity.com/paper/2206.12798/) (2022)
- [Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning](https://mlsanity.com/paper/2302.04061/) (2023)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [PathoTune: Adapting Visual Foundation Model to Pathological Specialists](https://mlsanity.com/paper/2403.16497/) (2024)
- [How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark](https://mlsanity.com/paper/2503.12990/) (2025)
- [Trustworthy Few-Shot Transfer of Medical VLMs through Split Conformal Prediction](https://mlsanity.com/paper/2506.17503/) (2025)
- [A multi-modal vision-language model for generalizable annotation-free pathology localization](https://mlsanity.com/paper/2401.02044/) (2024)
- [Few-Shot, Now for Real: Medical VLMs Adaptation without Balanced Sets or Validation](https://mlsanity.com/paper/2506.17500/) (2025)
- [H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression](https://mlsanity.com/paper/2201.06329/) (2022)
- [Performance Metrics for Probabilistic Ordinal Classifiers](https://mlsanity.com/paper/2309.08701/) (2023)

## Abstract (arXiv)

The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. In recent years, with the development of digitisation devices, the use of computer vision techniques for the analysis of biopsies has increased. However, to the best of the authors' knowledge, the development of algorithms to automatically detect individual cribriform patterns belonging to Gleason grade 4 has not yet been studied in the literature. The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies. The methodological core of this work is a patch-wise predictive model based on convolutional neural networks able to determine the presence of cancerous patterns. In particular, we train from scratch a simple self-design architecture. The cribriform pattern is detected by retraining the set of filters of the last convolutional layer in the network. From the reconstructed prediction map, we compute the percentage of each Gleason grade in the tissue to feed a multi-layer perceptron which provides a biopsy-level score.mIn our SICAPv2 database, composed of 182 annotated whole slide images, we obtained a Cohen's quadratic kappa of 0.77 in the test set for the patch-level Gleason grading with the proposed architecture trained from scratch. Our results outperform previous ones reported in the literature. Furthermore, this model reaches the level of fine-tuned state-of-the-art architectures in a patient-based four groups cross validation. In the cribriform pattern detection task, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason scoring, we achieved a quadratic Cohen's Kappa of 0.81 in the test subset. Shallow CNN architectures trained from scratch outperform current state-of-the-art methods for Gleason grades classification.