# MMLNB: Multi-Modal Learning for Neuroblastoma Subtyping Classification Assisted with Textual Description Generation

arXiv: https://arxiv.org/abs/2503.12927 · HTML: https://mlsanity.com/paper/2503.12927/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?](https://mlsanity.com/paper/2112.13906/) (2021)
- [HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification](https://mlsanity.com/paper/2209.10218/) (2022)
- [ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification](https://mlsanity.com/paper/2202.07570/) (2022)
- [MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant](https://mlsanity.com/paper/2403.04290/) (2024)
- [Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases](https://mlsanity.com/paper/2401.00926/) (2024)
- [SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms](https://mlsanity.com/paper/2406.09931/) (2024)
- [Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach](https://mlsanity.com/paper/2408.16343/) (2024)
- [FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification](https://mlsanity.com/paper/2407.15312/) (2024)
- [Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data](https://mlsanity.com/paper/2307.03575/) (2023)

## Built on by (most cited first, 3 of 3)

- [GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT](https://mlsanity.com/paper/2605.22619/) (2026)
- [Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction](https://mlsanity.com/paper/2510.06113/) (2025)
- [ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora](https://mlsanity.com/paper/2608.20369/) (2026)

## Abstract (arXiv)

Neuroblastoma (NB), a leading cause of childhood cancer mortality, exhibits significant histopathological variability, necessitating precise subtyping for accurate prognosis and treatment. Traditional diagnostic methods rely on subjective evaluations that are time-consuming and inconsistent. To address these challenges, we introduce MMLNB, a multi-modal learning (MML) model that integrates pathological images with generated textual descriptions to improve classification accuracy and interpretability. The approach follows a two-stage process. First, we fine-tune a Vision-Language Model (VLM) to enhance pathology-aware text generation. Second, the fine-tuned VLM generates textual descriptions, using a dual-branch architecture to independently extract visual and textual features. These features are fused via Progressive Robust Multi-Modal Fusion (PRMF) Block for stable training. Experimental results show that the MMLNB model is more accurate than the single modal model. Ablation studies demonstrate the importance of multi-modal fusion, fine-tuning, and the PRMF mechanism. This research creates a scalable AI-driven framework for digital pathology, enhancing reliability and interpretability in NB subtyping classification. Our source code is available at https://github.com/HovChen/MMLNB.