The sharp rise in non-alcoholic fatty liver disease (NAFLD) cases has become a major health concern in recent years. Accurately identifying tissue alteration regions is crucial for NAFLD diagnosis but challenging with small-scale pathology datasets. Recently, prompt tuning has emerged as an effective strategy for adapting vision models to small-scale data analysis. However, current prompting techniques, designed primarily for general image classification, use generic cues that are inadequate when dealing with the intricacies of pathological tissue analysis. To solve this problem, we introduce Quantitative Attribute-based Polarity Visual Prompting (Q-PoVP), a new prompting method for pathology image analysis. Q-PoVP introduces two types of measurable attributes: K-function-based spatial attributes and histogram-based morphological attributes. Both help to measure tissue conditions quantitatively. We develop a quantitative attribute-based polarity visual prompt generator that converts quantitative visual attributes into positive and negative visual prompts, facilitating a more comprehensive and nuanced interpretation of pathological images. To enhance feature discrimination, we introduce a novel orthogonal-based polarity visual prompt tuning technique that disentangles and amplifies positive visual attributes while suppressing negative ones. We extensively tested our method on three different tasks. Our task-specific prompting demonstrates superior performance in both diagnostic accuracy and interpretability compared to existing methods. This dual advantage makes it particularly valuable for clinical settings, where healthcare providers require not only reliable results but also transparent reasoning to support informed patient care decisions. Code is available at https://github.com/7LFB/Q-PoVP.

The increasing prevalence of non-alcoholic fatty liver disease (NAFLD) has caused public concern in recent years. The high prevalence and risk of severe complications make monitoring NAFLD progression a public health priority. Fibrosis staging from liver biopsy images plays a key role in demonstrating the histological progression of NAFLD. Fibrosis mainly involves the deposition of fibers around vessels. Current deep learning-based fi-brosis staging methods learn spatial relationships between tissue patches but do not explicitly consider the relation-ships between vessels and fibers, leading to limited performance and poor interpretability. In this paper, we propose an eXplicit vessel-fiber modeling method for Fibrosis staging from liver biopsy images, namely XFibrosis. Specifically, we transform vessels and fibers into graph-structured representations, where their micro-structures are depicted by vessel-induced primal graphs andfiber-induced dual graphs, respectively. Moreover, the fiber-induced dual graphs also represent the connectivity information between vessels caused by fiber deposition. A primal-dual graph convolution module is designed to facilitate the learning of spatial relationships between vessels and fibers, allowing for the joint exploration and interaction of their micro-structures. Experiments conducted on two datasets have shown that explicitly modeling the relationship between vessels and fibers leads to improved fibrosis staging and en-hanced interpretability.

The rapid increase in cases of non-alcoholic fatty liver disease (NAFLD) in recent years has raised significant public concern. Accurately identifying tissue alteration regions is crucial for the diagnosis of NAFLD, but this task presents challenges in pathology image analysis, particularly with small-scale datasets. Recently, the paradigm shift from full fine-tuning to prompting in adapting vision foundation models has offered a new perspective for small-scale data analysis. However, existing prompting methods based on task-agnostic prompts are mainly developed for generic image recognition, which fall short in providing instructive cues for complex pathology images. In this paper, we propose Quantitative Attribute-based Prompting (QAP), a novel prompting method specifically for liver pathology image analysis. QAP is based on two quantitative attributes, namely K-function-based spatial attributes and histogram-based morphological attributes, which are aimed for quantitative assessment of tissue states. Moreover, a conditional prompt generator is designed to turn these instance-specific attributes into visual prompts. Extensive experiments on three diverse tasks demonstrate that our task-specific prompting method achieves better diagnostic performance as well as better interpretability. Code is available at https://github.com/7LFBIQAP.

Liver biopsy images play a key role in the diagnosis of global non-alcoholic fatty liver disease (NAFLD). The NAFLD activity score (NAS) on liver biopsy images grades the amount of histological findings that reflect the progression of NAFLD. However, liver biopsy image analysis remains a challenging task due to its complex tissue structures and sparse distribution of histological findings. In this paper, we propose a sparse interpretable feature learning method (SparseX) to efficiently estimate NAS scores. First, we introduce an interpretable spatial sampling strategy based on histological features to effectively select informative tissue regions containing tissue alterations. Then, SparseX formulates the feature learning as a low-rank decomposition problem. Non-negative matrix factorization (NMF)-based attributes learning is embedded into a deep network to compress and select sparse features for a small portion of tissue alterations contributing to diagnosis. Experiments conducted on the internal Liver-NAS and public SteatosisRaw datasets show the effectiveness of the proposed method in terms of classification performance and interpretability. regions containing tissue alterations. Then, SparseX formulates the feature learning as a low-rank decomposition problem. Non-negative matrix factorization (NMF)-based attributes learning is embedded into a deep network to compress and select sparse features for a small portion of tissue alterations contributing to diagnosis. Experiments conducted on the internal Liver-NAS and public SteatosisRaw datasets show the effectiveness of the proposed method in terms of classification performance and interpretability.

Accurate prediction of mortality risk is important for evaluating early treatments, detecting high-risk patients and improving healthcare outcomes. Predicting mortality risk from the irregular clinical time series data is challenging due to the varying time intervals in the consecutive records. Existing methods usually solve this issue by generating regular time series data from the original irregular data without considering the uncertainty in the generated data, caused by varying time intervals. In this paper, we propose a novel Uncertainty-Aware Convolutional Recurrent Neural Network (UA-CRNN), which incorporates the uncertainty information in the generated data to improve the mortality risk prediction performance. To handle the complex clinical time series data with sub-series of different frequencies, we propose to incorporate the uncertainty information into the sub-series level rather than the whole time series data. Specifically, we design a novel hierarchical uncertainty-aware decomposition layer (UADL) to adaptively decompose time series into different sub-series and assign them proper weights according to their reliabilities. Experimental results on two real-world clinical datasets demonstrate that the proposed UA-CRNN method significantly outperforms state-of-the-art methods in both short-term and long-term mortality risk predictions.