SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation

Medical image segmentation data inherently contain uncertainty, often stemming from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which depend on annotators' expertise and the clinical context of the annotations. For instance, a boundary pixel might be labeled as tumor in diagnosis to avoid under-assessment of severity, but as normal tissue in radiotherapy to prevent damage to sensitive structures. As segmentation preferences vary across downstream applications, it is often desirable for an image segmentation model to offer user-adaptable predictions rather than a fixed output. While prior uncertainty-aware and interactive methods offer adaptability, they are inefficient at test time: uncertainty-aware models require users to choose from numerous similar outputs, while interactive models demand significant user input through click or box prompts to refine segmentation. To address these challenges, we propose \textbf{SPA}, a segmentation framework that efficiently adapts to diverse test-time preferences with minimal human interaction. By presenting users a select few, distinct segmentation candidates that best capture uncertainties, it reduces clinician workload in reaching the preferred segmentation. To accommodate user preference, we introduce a probabilistic mechanism that leverages user feedback to adapt model's segmentation preference. The proposed framework is evaluated on a diverse range of medical image segmentation tasks: color fundus images, CT, and MRI. It demonstrates 1) a significant reduction in clinician time and effort compared with existing interactive segmentation approaches, 2) strong adaptability based on human feedback, and 3) state-of-the-art image segmentation performance across diverse modalities and semantic labels.

Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning

Surgical video segmentation is a critical task in computer-assisted surgery and is vital for enhancing surgical quality and patient outcomes. Recently, the Segment Anything Model 2 (SAM2) framework has shown superior advancements in image and video segmentation. However, SAM2 struggles with efficiency due to the high computational demands of processing high-resolution images and complex and long-range temporal dynamics in surgical videos. To address these challenges, we introduce Surgical SAM 2 (SurgSAM-2), an advanced model to utilize SAM2 with an Efficient Frame Pruning (EFP) mechanism, to facilitate real-time surgical video segmentation. The EFP mechanism dynamically manages the memory bank by selectively retaining only the most informative frames, reducing memory usage and computational cost while maintaining high segmentation accuracy. Our extensive experiments demonstrate that SurgSAM-2 significantly improves both efficiency and segmentation accuracy compared to the vanilla SAM2. Remarkably, SurgSAM-2 achieves a 3$\times$ FPS compared with SAM2, while also delivering state-of-the-art performance after fine-tuning with lower-resolution data. These advancements establish SurgSAM-2 as a leading model for surgical video analysis, making real-time surgical video segmentation in resource-constrained environments a feasible reality.

Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation

We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large Language Model (LLM) capabilities and generating evidence-based results, thereby improving safety and reliability when handling private medical data. Our comprehensive pipeline begins with a hybrid static-semantic approach to document chunking, significantly improving context capture over traditional methods. Extracted entities are used to create a three-tier hierarchical graph structure, linking entities to foundational medical knowledge sourced from medical papers and dictionaries. These entities are then interconnected to form meta-graphs, which are merged based on semantic similarities to develop a comprehensive global graph. This structure supports precise information retrieval and response generation. The retrieval process employs a U-retrieve method to balance global awareness and indexing efficiency of the LLM. Our approach is validated through a comprehensive ablation study comparing various methods for document chunking, graph construction, and information retrieval. The results not only demonstrate that our hierarchical graph construction method consistently outperforms state-of-the-art models on multiple medical Q\&A benchmarks, but also confirms that the responses generated include source documentation, significantly enhancing the reliability of medical LLMs in practical applications. Code will be at: https://github.com/MedicineToken/Medical-Graph-RAG/tree/main

MedUHIP: Towards Human-In-the-Loop Medical Segmentation

Although segmenting natural images has shown impressive performance, these techniques cannot be directly applied to medical image segmentation. Medical image segmentation is particularly complicated by inherent uncertainties. For instance, the ambiguous boundaries of tissues can lead to diverse but plausible annotations from different clinicians. These uncertainties cause significant discrepancies in clinical interpretations and impact subsequent medical interventions. Therefore, achieving quantitative segmentations from uncertain medical images becomes crucial in clinical practice. To address this, we propose a novel approach that integrates an \textbf{uncertainty-aware model} with \textbf{human-in-the-loop interaction}. The uncertainty-aware model proposes several plausible segmentations to address the uncertainties inherent in medical images, while the human-in-the-loop interaction iteratively modifies the segmentation under clinician supervision. This collaborative model ensures that segmentation is not solely dependent on automated techniques but is also refined through clinician expertise. As a result, our approach represents a significant advancement in the field which enhances the safety of medical image segmentation. It not only offers a comprehensive solution to produce quantitative segmentation from inherent uncertain medical images, but also establishes a synergistic balance between algorithmic precision and clincian knowledge. We evaluated our method on various publicly available multi-clinician annotated datasets: REFUGE2, LIDC-IDRI and QUBIQ. Our method showcases superior segmentation capabilities, outperforming a wide range of deterministic and uncertainty-aware models. We also demonstrated that our model produced significantly better results with fewer interactions compared to previous interactive models. We will release the code to foster further research in this area.

Medical SAM 2: Segment medical images as video via Segment Anything Model 2

In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced segmentation model that utilizes the SAM 2 framework to address both 2D and 3D medical image segmentation tasks. By adopting the philosophy of taking medical images as videos, MedSAM-2 not only applies to 3D medical images but also unlocks new One-prompt Segmentation capability. That allows users to provide a prompt for just one or a specific image targeting an object, after which the model can autonomously segment the same type of object in all subsequent images, regardless of temporal relationships between the images. We evaluated MedSAM-2 across a variety of medical imaging modalities, including abdominal organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing it against state-of-the-art models in both traditional and interactive segmentation settings. Our findings show that MedSAM-2 not only surpasses existing models in performance but also exhibits superior generalization across a range of medical image segmentation tasks. Our code will be released at: https://github.com/MedicineToken/Medical-SAM2

MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging

Medicine is inherently a multimodal discipline. Medical images can reflect the pathological changes of cancer and tumors, while the expression of specific genes can influence their morphological characteristics. However, most deep learning models employed for these medical tasks are unimodal, making predictions using either image data or genomic data exclusively. In this paper, we propose a multimodal pre-training framework that jointly incorporates genomics and medical images for downstream tasks. To address the issues of high computational complexity and difficulty in capturing long-range dependencies in genes sequence modeling with MLP or Transformer architectures, we utilize Mamba to model these long genomic sequences. We aligns medical images and genes using a self-supervised contrastive learning approach which combines the Mamba as a genetic encoder and the Vision Transformer (ViT) as a medical image encoder. We pre-trained on the TCGA dataset using paired gene expression data and imaging data, and fine-tuned it for downstream tumor segmentation tasks. The results show that our model outperformed a wide range of related methods.

Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation

The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation. An efficient way to do so is by using Adapters, specialized modules that learn just a few parameters to tailor SAM specifically for medical images. However, unlike natural images, many tissues and lesions in medical images have blurry boundaries and may be ambiguous. Previous efforts to adapt SAM ignore this challenge and can only predict distinct segmentation. It may mislead clinicians or cause misdiagnosis, especially when encountering rare variants or situations with low model confidence. In this work, we propose a novel module called the Uncertainty-aware Adapter, which efficiently fine-tuning SAM for uncertainty-aware medical image segmentation. Utilizing a conditional variational autoencoder, we encoded stochastic samples to effectively represent the inherent uncertainty in medical imaging. We designed a new module on a standard adapter that utilizes a condition-based strategy to interact with samples to help SAM integrate uncertainty. We evaluated our method on two multi-annotated datasets with different modalities: LIDC-IDRI (lung abnormalities segmentation) and REFUGE2 (optic-cup segmentation). The experimental results show that the proposed model outperforms all the previous methods and achieves the new state-of-the-art (SOTA) on both benchmarks. We also demonstrated that our method can generate diverse segmentation hypotheses that are more realistic as well as heterogeneous.

Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition

Some visual recognition tasks are more challenging then the general ones as they require professional categories of images. The previous efforts, like fine-grained vision classification, primarily introduced models tailored to specific tasks, like identifying bird species or car brands with limited scalability and generalizability. This paper aims to design a scalable and explainable model to solve Professional Visual Recognition tasks from a generic standpoint. We introduce a biologically-inspired structure named Pro-NeXt and reveal that Pro-NeXt exhibits substantial generalizability across diverse professional fields such as fashion, medicine, and art-areas previously considered disparate. Our basic-sized Pro-NeXt-B surpasses all preceding task-specific models across 12 distinct datasets within 5 diverse domains. Furthermore, we find its good scaling property that scaling up Pro-NeXt in depth and width with increasing GFlops can consistently enhances its accuracy. Beyond scalability and adaptability, the intermediate features of Pro-NeXt achieve reliable object detection and segmentation performance without extra training, highlighting its solid explainability. We will release the code to foster further research in this area.

PromptUNet: Toward Interactive Medical Image Segmentation

Prompt-based segmentation, also known as interactive segmentation, has recently become a popular approach in image segmentation. A well-designed prompt-based model called Segment Anything Model (SAM) has demonstrated its ability to segment a wide range of natural images, which has sparked a lot of discussion in the community. However, recent studies have shown that SAM performs poorly on medical images. This has motivated us to design a new prompt-based segmentation model specifically for medical image segmentation. In this paper, we combine the prompted-based segmentation paradigm with UNet, which is a widly-recognized successful architecture for medical image segmentation. We have named the resulting model PromptUNet. In order to adapt the real-world clinical use, we expand the existing prompt types in SAM to include novel Supportive Prompts and En-face Prompts. We have evaluated the capabilities of PromptUNet on 19 medical image segmentation tasks using a variety of image modalities, including CT, MRI, ultrasound, fundus, and dermoscopic images. Our results show that PromptUNet outperforms a wide range of state-of-the-art (SOTA) medical image segmentation methods, including nnUNet, TransUNet, UNetr, MedSegDiff, and MSA. Code will be released at: https://github.com/WuJunde/PromptUNet.

PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.