# Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI

arXiv: https://arxiv.org/abs/2407.05796 · HTML: https://mlsanity.com/paper/2407.05796/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification](https://mlsanity.com/paper/2103.14267/) (2021)
- [Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks](https://mlsanity.com/paper/1611.05916/) (2016)
- [Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification](https://mlsanity.com/paper/2206.07389/) (2022)
- [Unimodal probability distributions for deep ordinal classification](https://mlsanity.com/paper/1705.05278/) (2017)
- [Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation](https://mlsanity.com/paper/2307.08279/) (2023)
- [ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification](https://mlsanity.com/paper/2307.04136/) (2023)
- [Strategising template-guided needle placement for MR-targeted prostate biopsy](https://mlsanity.com/paper/2207.10784/) (2022)
- [Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration](https://mlsanity.com/paper/2207.06189/) (2022)

## Built on by (most cited first, 4 of 4)

- [ProFound: A moderate-sized vision foundation model for multi-task prostate imaging](https://mlsanity.com/paper/2603.03961/) (2026)
- [Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging](https://mlsanity.com/paper/2604.00985/) (2026)
- [KOAL: Knowledge-Driven Prostate Cancer Grading with Ordinal-Aware Learning](https://mlsanity.com/paper/2607.06019/) (2026)
- [Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading](https://mlsanity.com/paper/2607.14720/) (2026)

## Abstract (arXiv)

The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer's potential for growth and metastasis. In clinical practice, pathologists determine the Gleason groups based on specimens obtained from ultrasound-guided biopsies. In this study, we investigate the feasibility of directly estimating the Gleason groups from MRI scans to reduce otherwise required biopsies. We identify two characteristics of this task, ordinality and the resulting dependent yet unknown variances between Gleason groups. In addition to the inter- / intra- observer variability in a multi-step Gleason scoring process based on the interpretation of Gleason patterns, our MR-based prediction is also subject to specimen sampling variance and, to a lesser degree, varying MR imaging protocols. To address this challenge, we propose a novel Poisson ordinal network (PON). PONs model the prediction using a Poisson distribution and leverages Poisson encoding and Poisson focal loss to capture a learnable dependency between ordinal classes (here, Gleason groups), rather than relying solely on the numerical ground-truth (e.g. Gleason Groups 1-5 or Gleason Scores 6-10). To improve this modelling efficacy, PONs also employ contrastive learning with a memory bank to regularise intra-class variance, decoupling the memory requirement of contrast learning from the batch size. Experimental results based on the images labelled by saturation biopsies from 265 prior-biopsy-blind patients, across two tasks demonstrate the superiority and effectiveness of our proposed method.