PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology

The emergence of large multimodal models has unlocked remarkable potential in AI, particularly in pathology. However, the lack of specialized, high-quality benchmark impeded their development and precise evaluation. To address this, we introduce PathMMU, the largest and highest-quality expert-validated pathology benchmark for LMMs. It comprises 33,573 multimodal multi-choice questions and 21,599 images from various sources, and an explanation for the correct answer accompanies each question. The construction of PathMMU capitalizes on the robust capabilities of GPT-4V, utilizing approximately 30,000 gathered image-caption pairs to generate Q\&As. Significantly, to maximize PathMMU's authority, we invite six pathologists to scrutinize each question under strict standards in PathMMU's validation and test sets, while simultaneously setting an expert-level performance benchmark for PathMMU. We conduct extensive evaluations, including zero-shot assessments of 14 open-sourced and three closed-sourced LMMs and their robustness to image corruption. We also fine-tune representative LMMs to assess their adaptability to PathMMU. The empirical findings indicate that advanced LMMs struggle with the challenging PathMMU benchmark, with the top-performing LMM, GPT-4V, achieving only a 51.7\% zero-shot performance, significantly lower than the 71.4\% demonstrated by human pathologists. After fine-tuning, even open-sourced LMMs can surpass GPT-4V with a performance of over 60\%, but still fall short of the expertise shown by pathologists. We hope that the PathMMU will offer valuable insights and foster the development of more specialized, next-generation LLMs for pathology.

Unleashing the Power of Prompt-driven Nucleus Instance Segmentation

Nuclear instance segmentation in histology images is crucial for a broad spectrum of clinical applications. Current prevailing nuclear instance segmentation algorithms rely on regression of nuclei contours, distance maps, watershed markers or a proxy nuclear representation of star-convex polygons. Consequently, these methods necessitate sophisticated post-processing operations to distinguish nuclei instances, which are commonly acknowledged to be error-prone and parameter-sensitive. Recently, the segment anything model (SAM) has earned attracted huge attention within the domain of medical image segmentation due to its impressive generalization ability and promptable property. Nevertheless, its potential on nuclear instance segmentation remains largely underexplored. In this paper, we present a novel prompt-driven framework that consists of a point prompter and a SAM for automatic nuclei instance segmentation. Specifically, the prompter learns to generate a unique point prompt for each nucleus while the SAM is fine tuned to output the corresponding mask of the cued nucleus. Furthermore, we propose to add adjacent nuclei as negative prompts to promote the model's ability to recognize overlapping nuclei. Without bells and whistles, our proposed method sets a new state-of-the-art performance on three challenging benchmarks. Our code is available at \textcolor{magenta}{\url{https://github.com/windygoo/PromptNucSeg}} .

MI-Gen: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images

Whole slide images are the foundation of digital pathology for the diagnosis and treatment of carcinomas. Writing pathology reports is laborious and error-prone for inexperienced pathologists. To reduce the workload and improve clinical automation, we investigate how to generate pathology reports given whole slide images. On the data end, we curated the largest WSI-text dataset (TCGA-PathoText). In specific, we collected nearly 10000 high-quality WSI-text pairs for visual-language models by recognizing and cleaning pathology reports which narrate diagnostic slides in TCGA. On the model end, we propose the multiple instance generative model (MI-Gen) which can produce pathology reports for gigapixel WSIs. We benchmark our model on the largest subset of TCGA-PathoText. Experimental results show our model can generate pathology reports which contain multiple clinical clues. Furthermore, WSI-text prediction can be seen as an approach of visual-language pre-training, which enables our model to be transferred to downstream diagnostic tasks like carcinoma grading and phenotyping. We observe that simple semantic extraction from the pathology reports can achieve the best performance (0.838 of F1 score) on BRCA subtyping without adding extra parameters or tricky fine-tuning. Our collected dataset and related code will all be publicly available.

Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis

Histopathology image analysis is the golden standard of clinical diagnosis for Cancers. In doctors daily routine and computer-aided diagnosis, the Whole Slide Image (WSI) of histopathology tissue is used for analysis. Because of the extremely large scale of resolution, previous methods generally divide the WSI into a large number of patches, then aggregate all patches within a WSI by Multi-Instance Learning (MIL) to make the slide-level prediction when developing computer-aided diagnosis tools. However, most previous WSI-MIL models using global-attention without pairwise interaction and any positional information, or self-attention with absolute position embedding can not well handle shape varying large WSIs, e.g. testing WSIs after model deployment may be larger than training WSIs, since the model development set is always limited due to the difficulty of histopathology WSIs collection. To deal with the problem, in this paper, we propose to amend position embedding for shape varying long-contextual WSI by introducing Linear Bias into Attention, and adapt it from 1-d long sequence into 2-d long-contextual WSI which helps model extrapolate position embedding to unseen or under-fitted positions. We further utilize Flash-Attention module to tackle the computational complexity of Transformer, which also keep full self-attention performance compared to previous attention approximation work. Our method, Long-contextual MIL (Long-MIL) are evaluated on extensive experiments including 4 dataset including WSI classification and survival prediction tasks to validate the superiority on shape varying WSIs. The source code will be open-accessed soon.

Test-Time Training for Semantic Segmentation with Output Contrastive Loss

Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge. To improve the model's generalization ability to the new domain during evaluation, the test-time training (TTT) is a challenging paradigm that adapts the source-pretrained model in an online fashion. Early efforts on TTT mainly focus on the image classification task. Directly extending these methods to semantic segmentation easily experiences unstable adaption due to segmentation's inherent characteristics, such as extreme class imbalance and complex decision spaces. To stabilize the adaptation process, we introduce contrastive loss (CL), known for its capability to learn robust and generalized representations. Nevertheless, the traditional CL operates in the representation space and cannot directly enhance predictions. In this paper, we resolve this limitation by adapting the CL to the output space, employing a high temperature, and simplifying the formulation, resulting in a straightforward yet effective loss function called Output Contrastive Loss (OCL). Our comprehensive experiments validate the efficacy of our approach across diverse evaluation scenarios. Notably, our method excels even when applied to models initially pre-trained using domain adaptation methods on test domain data, showcasing its resilience and adaptability.\footnote{Code and more information could be found at~ \url{https://github.com/dazhangyu123/OCL}}

Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification

Overfitting remains a significant challenge in the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis. Visualizing heatmaps reveals that current MIL methods focus on a subset of predictive instances, hindering effective model generalization. To tackle this, we propose Attention-Challenging MIL (ACMIL), aimed at forcing the attention mechanism to capture more challenging predictive instances. ACMIL incorporates two techniques, Multiple Branch Attention (MBA) to capture richer predictive instances and Stochastic Top-K Instance Masking (STKIM) to suppress simple predictive instances. Evaluation on three WSI datasets outperforms state-of-the-art methods. Additionally, through heatmap visualization, UMAP visualization, and attention value statistics, this paper comprehensively illustrates ACMIL's effectiveness in overcoming the overfitting challenge. The source code is available at \url{https://github.com/dazhangyu123/ACMIL}.

Exploring Unsupervised Cell Recognition with Prior Self-activation Maps

The success of supervised deep learning models on cell recognition tasks relies on detailed annotations. Many previous works have managed to reduce the dependency on labels. However, considering the large number of cells contained in a patch, costly and inefficient labeling is still inevitable. To this end, we explored label-free methods for cell recognition. Prior self-activation maps (PSM) are proposed to generate pseudo masks as training targets. To be specific, an activation network is trained with self-supervised learning. The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps. Afterward, a semantic clustering module is then introduced as a pipeline to transform PSMs to pixel-level semantic pseudo masks for downstream tasks. We evaluated our method on two histological datasets: MoNuSeg (cell segmentation) and BCData (multi-class cell detection). Compared with other fully-supervised and weakly-supervised methods, our method can achieve competitive performance without any manual annotations. Our simple but effective framework can also achieve multi-class cell detection which can not be done by existing unsupervised methods. The results show the potential of PSMs that might inspire other research to deal with the hunger for labels in medical area.

Semi-supervised Cell Recognition under Point Supervision

Cell recognition is a fundamental task in digital histopathology image analysis. Point-based cell recognition (PCR) methods normally require a vast number of annotations, which is extremely costly, time-consuming and labor-intensive. Semi-supervised learning (SSL) can provide a shortcut to make full use of cell information in gigapixel whole slide images without exhaustive labeling. However, research into semi-supervised point-based cell recognition (SSPCR) remains largely overlooked. Previous SSPCR works are all built on density map-based PCR models, which suffer from unsatisfactory accuracy, slow inference speed and high sensitivity to hyper-parameters. To address these issues, end-to-end PCR models are proposed recently. In this paper, we develop a SSPCR framework suitable for the end-to-end PCR models for the first time. Overall, we use the current models to generate pseudo labels for unlabeled images, which are in turn utilized to supervise the models training. Besides, we introduce a co-teaching strategy to overcome the confirmation bias problem that generally exists in self-training. A distribution alignment technique is also incorporated to produce high-quality, unbiased pseudo labels for unlabeled data. Experimental results on four histopathology datasets concerning different types of staining styles show the effectiveness and versatility of the proposed framework. Code is available at \textcolor{magenta}{\url{https://github.com/windygooo/SSPCR}

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation. However, the field of pathology has largely remained untapped in this regard, despite the growing need for accurate, timely, and personalized diagnostics. To bridge the gap in pathology MLLMs, we present the PathAsst in this study, which is a generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. To develop PathAsst, we collect over 142K high-quality pathology image-text pairs from a variety of reliable sources, including PubMed, comprehensive pathology textbooks, reputable pathology websites, and private data annotated by pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data, specifically tailored for the invocation of the pathology-specific models, allowing the PathAsst to effectively interact with these models based on the input image and user intent, consequently enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is trained based on Vicuna-13B language model in coordination with the CLIP vision encoder. The results of PathAsst show the potential of harnessing the AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We are committed to open-sourcing our meticulously curated dataset, as well as a comprehensive toolkit designed to aid researchers in the extensive collection and preprocessing of their own datasets. Resources can be obtained at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.