MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale

Medical Visual Question Answering (MedVQA), which offers language responses to image-based medical inquiries, represents a challenging task and significant advancement in healthcare. It assists medical experts to swiftly interpret medical images, thereby enabling faster and more accurate diagnoses. However, the model interpretability and transparency of existing MedVQA solutions are often limited, posing challenges in understanding their decision-making processes. To address this issue, we devise a semi-automated annotation process to streamlining data preparation and build new benchmark MedVQA datasets R-RAD and R-SLAKE. The R-RAD and R-SLAKE datasets provide intermediate medical decision-making rationales generated by multimodal large language models and human annotations for question-answering pairs in existing MedVQA datasets, i.e., VQA-RAD and SLAKE. Moreover, we design a novel framework which finetunes lightweight pretrained generative models by incorporating medical decision-making rationales into the training process. The framework includes three distinct strategies to generate decision outcomes and corresponding rationales, thereby clearly showcasing the medical decision-making process during reasoning. Extensive experiments demonstrate that our method can achieve an accuracy of 83.5% on R-RAD and 86.3% on R-SLAKE, significantly outperforming existing state-of-the-art baselines. Dataset and code will be released.

Group-On: Boosting One-Shot Segmentation with Supportive Query

One-shot semantic segmentation aims to segment query images given only ONE annotated support image of the same class. This task is challenging because target objects in the support and query images can be largely different in appearance and pose (i.e., intra-class variation). Prior works suggested that incorporating more annotated support images in few-shot settings boosts performances but increases costs due to additional manual labeling. In this paper, we propose a novel approach for ONE-shot semantic segmentation, called Group-On, which packs multiple query images in batches for the benefit of mutual knowledge support within the same category. Specifically, after coarse segmentation masks of the batch of queries are predicted, query-mask pairs act as pseudo support data to enhance mask predictions mutually, under the guidance of a simple Group-On Voting module. Comprehensive experiments on three standard benchmarks show that, in the ONE-shot setting, our Group-On approach significantly outperforms previous works by considerable margins. For example, on the COCO-20i dataset, we increase mIoU scores by 8.21% and 7.46% on ASNet and HSNet baselines, respectively. With only one support image, Group-On can be even competitive with the counterparts using 5 annotated support images.

Personalized Heart Disease Detection via ECG Digital Twin Generation

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals' anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development.

Multi-rater Prompting for Ambiguous Medical Image Segmentation

Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters). In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for re-training the entire model on a different dataset domain. We propose a multi-rater prompt-based approach to address these two challenges altogether. Specifically, we introduce a series of rater-aware prompts that can be plugged into the U-Net model for uncertainty estimation to handle multi-annotation cases. During the prompt-based fine-tuning process, only 0.3% of learnable parameters are required to be updated comparing to training the entire model. Further, in order to integrate expert consensus and disagreement, we explore different multi-rater incorporation strategies and design a mix-training strategy for comprehensive insight learning. Extensive experiments verify the effectiveness of our new approach for ambiguous medical image segmentation on two public datasets while alleviating the heavy burden of model re-training.

Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) such as GPT-4V and Gemini Pro face challenges in achieving human-level perception in Visual Question Answering (VQA), particularly in object-oriented perception tasks which demand fine-grained understanding of object identities, locations or attributes, as indicated by empirical findings. This is mainly due to their limited capability to effectively integrate complex visual cues with textual information and potential object hallucinations. In this paper, we present a novel approach, Joint Visual and Text Prompting (VTPrompt), that employs fine-grained visual information to enhance the capability of MLLMs in VQA, especially for object-oriented perception. VTPrompt merges visual and text prompts to extract key concepts from textual questions and employs a detection model to highlight relevant objects as visual prompts in images. The processed images alongside text prompts are subsequently fed into MLLMs to produce more accurate answers. Our experiments with GPT-4V and Gemini Pro, on three benchmarks, i.e., MME , MMB and POPE, demonstrate significant improvements. Particularly, our method led to a score improvement of up to 183.5 for GPT-4V on MME and enhanced MMB performance by 8.17\% for GPT-4V and 15.69\% for Gemini Pro.

TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model

Recently, there has been a burgeoning interest in virtual clinical trials, which simulate real-world scenarios and hold the potential to significantly enhance patient safety, expedite development, reduce costs, and contribute to the broader scientific knowledge in healthcare. Existing research often focuses on leveraging electronic health records (EHRs) to support clinical trial outcome prediction. Yet, trained with limited clinical trial outcome data, existing approaches frequently struggle to perform accurate predictions. Some research has attempted to generate EHRs to augment model development but has fallen short in personalizing the generation for individual patient profiles. Recently, the emergence of large language models has illuminated new possibilities, as their embedded comprehensive clinical knowledge has proven beneficial in addressing medical issues. In this paper, we propose a large language model-based digital twin creation approach, called TWIN-GPT. TWIN-GPT can establish cross-dataset associations of medical information given limited data, generating unique personalized digital twins for different patients, thereby preserving individual patient characteristics. Comprehensive experiments show that using digital twins created by TWIN-GPT can boost clinical trial outcome prediction, exceeding various previous prediction approaches. Besides, we also demonstrate that TWIN-GPT can generate high-fidelity trial data that closely approximate specific patients, aiding in more accurate result predictions in data-scarce situations. Moreover, our study provides practical evidence for the application of digital twins in healthcare, highlighting its potential significance.

PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis

Automatic ophthalmic disease diagnosis on fundus images is important in clinical practice. However, due to complex fundus textures and limited annotated data, developing an effective automatic method for this problem is still challenging. In this paper, we present a self-supervised method via polar transformation based progressive contrastive learning, called PoCo, for ophthalmic disease diagnosis. Specifically, we novelly inject the polar transformation into contrastive learning to 1) promote contrastive learning pre-training to be faster and more stable and 2) naturally capture task-free and rotation-related textures, which provides insights into disease recognition on fundus images. Beneficially, simple normal translation-invariant convolution on transformed images can equivalently replace the complex rotation-invariant and sector convolution on raw images. After that, we develop a progressive contrastive learning method to efficiently utilize large unannotated images and a novel progressive hard negative sampling scheme to gradually reduce the negative sample number for efficient training and performance enhancement. Extensive experiments on three public ophthalmic disease datasets show that our PoCo achieves state-of-the-art performance with good generalization ability, validating that our method can reduce annotation efforts and provide reliable diagnosis. Codes are available at \url{https://github.com/wjh892521292/PoCo}.

DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM

We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini. Our approach consists of a detection prompting toolkit inspired by high-precision detection priors and a new Chain-of-Thought to implement these prompts. Specifically, the prompts in the toolkit are designed to guide the MLLM to focus on regional information (e.g., zooming in), read coordinates according to measure standards (e.g., overlaying rulers and compasses), and infer from the contextual information (e.g., overlaying scene graphs). Building upon these tools, the new detection chain-of-thought can automatically decompose the task into simple subtasks, diagnose the predictions, and plan for progressive box refinements. The effectiveness of our framework is demonstrated across a spectrum of detection tasks, especially hard cases. Compared to existing state-of-the-art methods, GPT-4V with our DetToolChain improves state-of-the-art object detectors by +21.5% AP50 on MS COCO Novel class set for open-vocabulary detection, +24.23% Acc on RefCOCO val set for zero-shot referring expression comprehension, +14.5% AP on D-cube describe object detection FULL setting.

Making Pre-trained Language Models Great on Tabular Prediction

The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. However, due to the heterogeneity among tables, such DNN bonus is still far from being well exploited on tabular data prediction (e.g., regression or classification tasks). Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables. In this paper, we present TP-BERTa, a specifically pre-trained LM for tabular data prediction. Concretely, a novel relative magnitude tokenization converts scalar numerical feature values to finely discrete, high-dimensional tokens, and an intra-feature attention approach integrates feature values with the corresponding feature names. Comprehensive experiments demonstrate that our pre-trained TP-BERTa leads the performance among tabular DNNs and is competitive with Gradient Boosted Decision Tree models in typical tabular data regime.

Large Window-based Mamba UNet for Medical Image Segmentation: Beyond Convolution and Self-attention

In clinical practice, medical image segmentation provides useful information on the contours and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and treatment. In the past few years, convolutional neural networks (CNNs) and Transformers have dominated this area, but they still suffer from either limited receptive fields or costly long-range modeling. Mamba, a State Space Sequence Model (SSM), recently emerged as a promising paradigm for long-range dependency modeling with linear complexity. In this paper, we introduce a Large Window-based Mamba U}-shape Network, or LMa-UNet, for 2D and 3D medical image segmentation. A distinguishing feature of our LMa-UNet is its utilization of large windows, excelling in locally spatial modeling compared to small kernel-based CNNs and small window-based Transformers, while maintaining superior efficiency in global modeling compared to self-attention with quadratic complexity. Additionally, we design a novel hierarchical and bidirectional Mamba block to further enhance the global and neighborhood spatial modeling capability of Mamba. Comprehensive experiments demonstrate the effectiveness and efficiency of our method and the feasibility of using large window size to achieve large receptive fields. Codes are available at https://github.com/wjh892521292/LMa-UNet.