# Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification

arXiv: https://arxiv.org/abs/2406.05596 · HTML: https://mlsanity.com/paper/2406.05596/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing](https://mlsanity.com/paper/2204.09817/) (2022)
- [Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification](https://mlsanity.com/paper/2211.11158/) (2022)
- [UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2107.00781/) (2021)
- [Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation](https://mlsanity.com/paper/2306.02416/) (2023)
- [Data-Centric Foundation Models in Computational Healthcare: A Survey](https://mlsanity.com/paper/2401.02458/) (2024)
- [FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images](https://mlsanity.com/paper/1907.12056/) (2019)
- [FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images](https://mlsanity.com/paper/2104.01771/) (2021)
- [Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime](https://mlsanity.com/paper/2303.17644/) (2023)

## Built on by (most cited first, 18 of 18)

- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.18378/) (2025)
- [Cost-effective Instruction Learning for Pathology Vision and Language Analysis](https://mlsanity.com/paper/2407.17734/) (2024)
- [MVP-CBM:Multi-layer Visual Preference-enhanced Concept Bottleneck Model for Explainable Medical Image Classification](https://mlsanity.com/paper/2506.12568/) (2025)
- [Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation](https://mlsanity.com/paper/2511.08402/) (2025)
- [Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration](https://mlsanity.com/paper/2505.02848/) (2025)
- [Data-Efficient Fine-Tuning of Vision-Language Models for Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2509.07613/) (2025)
- [LUCID-SAE: Learning Unified Vision-Language Sparse Codes for Interpretable Concept Discovery](https://mlsanity.com/paper/2602.07311/) (2026)
- [CoPA: Hierarchical Concept Prompting and Aggregating Network for Explainable Diagnosis](https://mlsanity.com/paper/2510.03767/) (2025)
- [Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation](https://mlsanity.com/paper/2511.05841/) (2025)
- [ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining](https://mlsanity.com/paper/2511.22866/) (2025)
- [A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding](https://mlsanity.com/paper/2512.21414/) (2025)
- [Coverage-aware Semantic Representation Learning for Underrepresented Visual Concepts](https://mlsanity.com/paper/2602.16917/) (2026)
- [Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models](https://mlsanity.com/paper/2602.20511/) (2026)
- [Towards Fine-Grained and Verifiable Concept Bottleneck Models](https://mlsanity.com/paper/2605.14210/) (2026)
- [Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework](https://mlsanity.com/paper/2607.01648/) (2026)
- [Boot-and-Feedback Framework for Generalist-Expert Model Collaboration in Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2608.23974/) (2026)
- [Medical AI Encodes a "Feeling of Error": Verifying Cancer Segmentation via Internal Concepts](https://mlsanity.com/paper/2609.08879/) (2026)

## Abstract (arXiv)

Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process. In this study, we investigate the explainable model development that can mimic the decision-making process of human experts by fusing the domain knowledge of explicit diagnostic criteria. We introduce a simple yet effective framework, Explicd, towards Explainable language-informed criteria-based diagnosis. Explicd initiates its process by querying domain knowledge from either large language models (LLMs) or human experts to establish diagnostic criteria across various concept axes (e.g., color, shape, texture, or specific patterns of diseases). By leveraging a pretrained vision-language model, Explicd injects these criteria into the embedding space as knowledge anchors, thereby facilitating the learning of corresponding visual concepts within medical images. The final diagnostic outcome is determined based on the similarity scores between the encoded visual concepts and the textual criteria embeddings. Through extensive evaluation of five medical image classification benchmarks, Explicd has demonstrated its inherent explainability and extends to improve classification performance compared to traditional black-box models. Code is available at urlhttps://github.com/yhygao/Explicd.