Universal Debiased Editing on Foundation Models for Fair Medical Image Classification

In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while using FM API, particularly spurious correlations between pixels and sensitive attributes. Traditional methods for bias mitigation face limitations due to the restricted access to web-hosted FMs and difficulties in addressing the underlying bias encoded within the FM API. We propose an U(niversal) D(ebiased) E(diting) strategy, termed UDE, which generates UDE noise to mask such spurious correlation. UDE is capable of mitigating bias both within the FM API embedding and the images themselves. Furthermore, UDE is suitable for both white-box and black-box FM APIs, where we introduced G(reedy) (Z)eroth-O(rder) (GeZO) optimization for it when the gradient is inaccessible in black-box APIs. Our whole pipeline enables fairness-aware image editing that can be applied across various medical contexts without requiring direct model manipulation or significant computational resources. Our empirical results demonstrate the method's effectiveness in maintaining fairness and utility across different patient groups and diseases. In the era of AI-driven medicine, this work contributes to making healthcare diagnostics more equitable, showcasing a practical solution for bias mitigation in pre-trained image FMs.

Universal Debiased Editing for Fair Medical Image Classification

In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while using FM API, particularly spurious correlations between pixels and sensitive attributes. Traditional methods for bias mitigation face limitations due to the restricted access to web-hosted FMs and difficulties in addressing the underlying bias encoded within the FM API. We propose an U(niversal) D(ebiased) E(diting) strategy, termed UDE, which generates UDE noise to mask such spurious correlation. UDE is capable of mitigating bias both within the FM API embedding and the images themselves. Furthermore, UDE is suitable for both white-box and black-box FM APIs, where we introduced G(reedy) (Z)eroth-O(rder) (GeZO) optimization for it when the gradient is inaccessible in black-box APIs. Our whole pipeline enables fairness-aware image editing that can be applied across various medical contexts without requiring direct model manipulation or significant computational resources. Our empirical results demonstrate the method's effectiveness in maintaining fairness and utility across different patient groups and diseases. In the era of AI-driven medicine, this work contributes to making healthcare diagnostics more equitable, showcasing a practical solution for bias mitigation in pre-trained image FMs.

Content Conditional Debiasing for Fair Text Embedding

Mitigating biases in machine learning models has gained increasing attention in Natural Language Processing (NLP). Yet, only a few studies focus on fair text embeddings, which are crucial yet challenging for real-world applications. In this paper, we propose a novel method for learning fair text embeddings. We achieve fairness while maintaining utility trade-off by ensuring conditional independence between sensitive attributes and text embeddings conditioned on the content. Specifically, we enforce that embeddings of texts with different sensitive attributes but identical content maintain the same distance toward the embedding of their corresponding neutral text. Furthermore, we address the issue of lacking proper training data by using Large Language Models (LLMs) to augment texts into different sensitive groups. Our extensive evaluations demonstrate that our approach effectively improves fairness while preserving the utility of embeddings, representing a pioneering effort in achieving conditional independence for fair text embeddings.

Heterogeneous Federated Learning with Group-Aware Prompt Tuning

Transformers have achieved remarkable success in various machine-learning tasks, prompting their widespread adoption. In this paper, we explore their application in the context of federated learning (FL), with a particular focus on heterogeneous scenarios where individual clients possess diverse local datasets. To meet the computational and communication demands of FL, we leverage pre-trained Transformers and use an efficient prompt-tuning strategy. Our strategy introduces the concept of learning both shared and group prompts, enabling the acquisition of universal knowledge and group-specific knowledge simultaneously. Additionally, a prompt selection module assigns personalized group prompts to each input, aligning the global model with the data distribution of each client. This approach allows us to train a single global model that can automatically adapt to various local client data distributions without requiring local fine-tuning. In this way, our proposed method effectively bridges the gap between global and personalized local models in Federated Learning and surpasses alternative approaches that lack the capability to adapt to previously unseen clients. The effectiveness of our approach is rigorously validated through extensive experimentation and ablation studies.

Clinical-Inspired Cytological Whole Slide Image Screening with Just Slide-Level Labels

Cytology test is effective, non-invasive, convenient, and inexpensive for clinical cancer screening. ThinPrep, a commonly used liquid-based specimen, can be scanned to generate digital whole slide images (WSIs) for cytology testing. However, WSIs classification with gigapixel resolutions is highly resource-intensive, posing significant challenges for automated medical image analysis. In order to circumvent this computational impasse, existing methods emphasize learning features at the cell or patch level, typically requiring labor-intensive and detailed manual annotations, such as labels at the cell or patch level. Here we propose a novel automated Label-Efficient WSI Screening method, dubbed LESS, for cytology-based diagnosis with only slide-level labels. Firstly, in order to achieve label efficiency, we suggest employing variational positive-unlabeled (VPU) learning, enhancing patch-level feature learning using WSI-level labels. Subsequently, guided by the clinical approach of scrutinizing WSIs at varying fields of view and scales, we employ a cross-attention vision transformer (CrossViT) to fuse multi-scale patch-level data and execute WSI-level classification. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and FNAC 2019 dataset with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on a urine cytology WSI dataset, and 96.53%, 96.37%, 99.31%, 94.95% on FNAC 2019 dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art methods and realizes automatic cytology-based bladder cancer screening.

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging.