While hyperspectral images (HSI) benefit from numerous spectral channels that provide rich information for classification, the increased dimensionality and sensor variability make them more sensitive to distributional discrepancies across domains, which in turn can affect classification performance. To tackle this issue, hyperspectral single-source domain generalization (SDG) typically employs data augmentation to simulate potential domain shifts and enhance model robustness under the condition of single-source domain training data availability. However, blind augmentation may produce samples misaligned with real-world scenarios, while excessive emphasis on realism can suppress diversity, highlighting a tradeoff between realism and diversity that limits generalization to target domains. To address this challenge, we propose a spectral property-driven data augmentation (SPDDA) that explicitly accounts for the inherent properties of HSI, namely the device-dependent variation in the number of spectral channels and the mixing of adjacent channels. Specifically, SPDDA employs a spectral diversity module that resamples data from the source domain along the spectral dimension to generate samples with varying spectral channels, and constructs a channel-wise adaptive spectral mixer by modeling inter-channel similarity, thereby avoiding fixed augmentation patterns. To further enhance the realism of the augmented samples, we propose a spatial-spectral co-optimization mechanism, which jointly optimizes a spatial fidelity constraint and a spectral continuity self-constraint. Moreover, the weight of the spectral self-constraint is adaptively adjusted based on the spatial counterpart, thus preventing over-smoothing in the spectral dimension and preserving spatial structure. Extensive experiments conducted on three remote sensing benchmarks demonstrate that SPDDA outperforms state-of-the-art methods.

Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported. To overcome these challenges, we propose an interpretable deep learning pipeline, named Multi-Biomarker Histomorphology Discoverer (Multi-Beholder), to predict the status of five biomarkers in LGG using only hematoxylin and eosin-stained whole slide images. Specifically, Multi-Beholder incorporates one-class classification into the multiple instance learning framework to achieve accurate instance-level pseudo-labeling, thereby complementing slide-level labels and improving prediction performance. Multi-Beholder demonstrates high performance on two LGG cohorts with diverse races and scanning protocols, with area under the receiver operating characteristic curve up to 0.973 on the internal-validated TCGA-LGG dataset and 0.820 on the external-validated Xiangya cohort. Moreover, the interpretability of Multi-Beholder allows for discovering quantitative and qualitative correlations between biomarker status and histomorphology characteristics. Our pipeline not only provides a novel approach for biomarker prediction, enhancing the applicability of molecular treatments for LGG patients but also facilitates the discovery of new mechanisms in molecular functionality and LGG progression. Code can be accessed athttps://github.com/Vison307/Multi-Beholder.

Over the last few decades, the integration of AI-driven computational techniques into digital pathology has revolutionized survival prediction tasks. However, most existing methods in survival analysis discretize the entire survival period into predefined intervals, overlooking the inherent uncertainty in event occurrence and the heterogeneity of patient survival times. The censored data further exacerbate these challenges, amplifying uncertainty and variability. To address these limitations, we introduce the Dirichlet distribution to model discretized outputs as continuous probability distributions, providing a more accurate representation of uncertainty awareness. Building upon this foundation, we propose a universal multi-modal survival analysis loss function that leverages uncertainty-driven fusion. Our Uncertainty-Aware Multi-Modal Survival Analysis (UMSA) framework further explores the interactions between multi-scale pathological images and genomic data, providing promising insights into multi-modal survival analysis. Experimental evaluations on five publicly available datasets demonstrate that UMSA achieves state-of-the-art performance, validating its effectiveness and scalability in survival prediction tasks.

Immunohistochemistry (IHC) examination is essential for characterizing tumor subtypes, providing prognostic information, and developing personalized treatment plans. However, IHC staining preparation is more complex and expensive compared to Hematoxylin and Eosin (H&E) staining, limiting its widespread clinical application. Transforming H&E images into IHC images presents a promising solution. In this paper, we propose OT-StainNet, a novel virtual IHC staining method. OT-StainNet employs a pre-trained diffusion model with richer prior knowledge as the generator and fine-tunes it with LoRA adapters through adversarial training. Given that adjacent images of the same tissue stained with H&E and IHC are not precisely aligned at the pixel level, existing methods struggle to fully utilize the supervisory information from weakly paired IHC images. To address this issue, we propose an optimal transport-driven semantic matching (OTSM) mechanism, establishing accurate semantic correspondences between H&E-IHC image pairs. By leveraging the real IHC features obtained through the OTSM mechanism, we design a semantic consistency constraint (SCC) to ensure that the correlations among virtual IHC features remain consistent with those among real IHC features, thereby preserving valuable correlation information during stain transfer. We validate OT-StainNet using four types of IHC staining across two datasets. Extensive experiments demonstrate the effectiveness of our method compared to state-of-the-art approaches.

Low-cost accelerometers play a crucial role in modern society due to their advantages of small size, ease of integration, wearability, and mass production, making them widely applicable in automotive systems, aerospace, and wearable technology. However, this widely used sensor suffers from severe accuracy and range limitations. To this end, we propose a honed-energy regularized and optimal supervised GAN (HEROS-GAN), which transforms low-cost sensor signals into high-cost equivalents, thereby overcoming the precision and range limitations of low-cost accelerometers. Due to the lack of frame-level paired low-cost and high-cost signals for training, we propose an Optimal Transport Supervision (OTS), which leverages optimal transport theory to explore potential consistency between unpaired data, thereby maximizing supervisory information. Moreover, we propose a Modulated Laplace Energy (MLE), which injects appropriate energy into the generator to encourage it to break range limitations, enhance local changes, and enrich signal details. Given the absence of a dedicated dataset, we specifically establish a Low-cost Accelerometer Signal Enhancement Dataset (LASED) containing tens of thousands of samples, which is the first dataset serving to improve the accuracy and range of accelerometers and is released in Github. Experimental results demonstrate that a GAN combined with either OTS or MLE alone can surpass the previous signal enhancement SOTA methods by an order of magnitude. Integrating both OTS and MLE, the HEROS-GAN achieves remarkable results, which doubles the accelerometer range while reducing signal noise by two orders of magnitude, establishing a benchmark in the accelerometer signal processing.

Nuclei segmentation and classification provide an essential basis for tumor immune microenvironment analysis. The previous nuclei segmentation and classification models require splitting large images into smaller patches for training, leading to two significant issues. First, nuclei at the borders of adjacent patches often misalign during inference. Second, this patch-based approach significantly increases the model's training and inference time. Recently, Mamba has garnered attention for its ability to model large-scale images with linear time complexity and low memory consumption. It offers a promising solution for training nuclei segmentation and classification models on full-sized images. However, the Mamba orientation-based scanning method lacks account for category-specific features, resulting in suboptimal performance in scenarios with imbalanced class distributions. To address these challenges, this paper introduces a novel scanning strategy based on category probability sorting, which independently ranks and scans features for each category according to confidence from high to low. This approach enhances the feature representation of uncertain samples and mitigates the issues caused by imbalanced distributions. Extensive experiments conducted on four public datasets demonstrate that our method outperforms state-of-the-art approaches, delivering superior performance in nuclei segmentation and classification tasks.

High-quality pathology images are essential for accurate clinical diagnosis and treatment. However, acquiring high-resolution (HR) pathology images is often hindered by equipment limitations, limited expert availability, and complex slide preparation procedures. Image super-resolution (SR), which reconstructs HR images from low-resolution (LR) inputs, offers a practical solution. However, most existing SR methods are designed for natural images and often struggle to capture the distinct structural characteristics of pathology data. In this paper, we propose Context-Aware Generative Adversarial Network(CAGAN), a novel SR framework tailored for pathological images. It introduces a context path that effectively leverages the rich spatial context in whole slide images (WSIs) while maintaining computational efficiency. In addition, considering the significant differences in staining patterns and reconstruction difficulty between the nucleus and cytoplasm, we propose a Nucleus-Enhanced Hematoxylin Channel (NEHC) loss. This loss imposes targeted constraints on nuclei to better preserve morphological consistency. Experiments on two pathological datasets demonstrate that CA-GAN achieves state-of-the-art performance in both quantitative metrics and perceptual quality. Code will be available soon.

Immunohistochemistry (IHC) examination is essential for determining breast cancer subtypes and provides critical prognostic factors to guide treatment decisions. However, the complex and expensive preparation of IHC staining limits its widespread use in clinical practice. Recent advancements in generative models have introduced virtual staining as a promising alternative, yet obtaining pixel-level paired data in clinical settings remains a significant challenge. In this paper, we propose Multi-IHC Net, which utilizes unpaired data to simultaneously generate Ki67, ER, PR, and HER2 images from H&E-stained breast tissue. Specifically, a general encoder extracts generalized features from H&E images, while interactive decoders reconstruct the four types of IHC images. Additionally, a feature alignment module also models the correlations among the different IHC stains. To enhance accuracy, we introduce a pathology consistency mechanism between H&E and adjacent IHC images. Extensive experiments demonstrate the superiority of our method compared to state-of-the-art approaches.

Cell instance segmentation is critical to analyzing biomedical images, yet accurately distinguishing tightly touching cells remains a persistent challenge. Existing instance segmentation frameworks, including detection-based, contour-based, and distance mapping-based approaches, have made significant progress, but balancing model performance with computational efficiency remains an open problem. In this paper, we propose a novel cell instance segmentation method inspired by the four-color theorem. By conceptualizing cells as countries and tissues as oceans, we introduce a four-color encoding scheme that ensures adjacent instances receive distinct labels. This reformulation transforms instance segmentation into a constrained semantic segmentation problem with only four predicted classes, substantially simplifying the instance differentiation process. To solve the training instability caused by the non-uniqueness of four-color encoding, we design an asymptotic training strategy and encoding transformation method. Extensive experiments on various modes demonstrate our approach achieves state-of-the-art performance. The code is available at https://github.com/zhangye-zoe/FCIS.

Weakly supervised segmentation methods have garnered considerable attention due to their potential to alleviate the need for labor-intensive pixel-level annotations during model training. Traditional weakly supervised nuclei segmentation approaches typically involve a two-stage process: pseudo-label generation followed by network training. The performance of these methods is highly dependent on the quality of the generated pseudo-labels, which can limit their effectiveness. In this paper, we propose a novel domain-adaptive weakly supervised nuclei segmentation framework that addresses the challenge of pseudo-label generation through cross-task interaction strategies. Specifically, our approach leverages weakly annotated data to train an auxiliary detection task, which facilitates domain adaptation of the segmentation network. To improve the efficiency of domain adaptation, we introduce a consistent feature constraint module that integrates prior knowledge from the source domain. Additionally, we develop methods for pseudo-label optimization and interactive training to enhance domain transfer capabilities. We validate the effectiveness of our proposed method through extensive comparative and ablation experiments conducted on six datasets. The results demonstrate that our approach outperforms existing weakly supervised methods and achieves performance comparable to or exceeding that of fully supervised methods. Our code is available athttps://github.com/zhangye-zoe/DAWN.

In an increasingly digital world, there is an unprecedented demand in intelligent society for biometric recognition systems that balance security with convenience. Traditional methods, such as passwords and PINs, are vulnerable to breaches and impose the burden of remembering multiple credentials. To this end, we propose a handwriting verification technology leveraging inertial sensors embedded in wearable devices (e.g., smartphones). Handwriting biometrics offer enhanced security as unique writing patterns are inherently resistant to replication and forgery. However, this approach faces two critical challenges: Identity verification independent of written content, and generalizability to unseen writers during deployment. We therefore devise a feature-guided zero-shot learning (FGZSL) framework, which constructs a unique feature vector for each sample and then performs identity verification by comparing these feature vectors. For the first challenge, we design an aim focuser, a module that filters out irrelevant content from the data features, allowing the FGZSL to focus on identity-specific information rather than writing content. For the second challenge, we design a plug-and-play Rényi-entropy-based representation regularization, which constructs informative and discriminative features for seen categories during training. These features serve as bases for representing unseen categories during testing. We contribute the first inertial identity detection dataset, publicly available on GitHub, containing 39 800 training samples and 10 000 test samples. Extensive experiments demonstrate that the FGZSL framework outperforms existing methods in both seen and unseen categories, but also sets a new standard for secure and reliable identity authentication using inertial sensors.

As motion-sensing components, multimodal inertial sensors composed of accelerometers and gyroscopes are recognized for their compact size, low cost, and broad applications in wearable and smart devices, but they are affected by severe noise. Wavelet transform is renowned for its flexibility in analyzing signals due to its diverse wavelet bases, allowing it to adapt to different signal characteristics. However, the diverse signal noises challenge wavelet allocation. Moreover, the modal differences between acceleration and gyroscope signals make it difficult to share the same wavelet, adding complexity to the wavelet allocation for multimodal signals. To this end, we propose an OT-Mamba framework, which leverages Mamba to extract signal features. Mamba is specifically designed to handle ultra-long sequences, allowing it to capture long-range temporal dependencies for better wavelet selection. Considering the heterogeneity and potential synergy between the accelerometer and gyroscope signals, an optimal transport interaction is proposed to mine their relationship for collaborative wavelet selection. The proposed OT-Mamba combines the reliability of wavelet-based methods and the flexibility of deep learning approaches. As a weakly supervised method, OT-Mamba achieves superior performance compared to existing methods (including fully supervised ones) and outperforms the current state-of-the-art method by an order of magnitude across all quantitative metrics and downstream tasks.

Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid the expensive and laborious acquisition of pixel-level annotations, a wide range of studies attempt to adopt the class activation map (CAM), a weakly-supervised learning scheme, to achieve pixel-level tissue segmentation. However, CAM-based methods are prone to suffer from under-activation and over-activation issues, leading to poor segmentation performance. To address this problem, we propose a novel weakly-supervised semantic segmentation framework for histopathological images based on image-mixing synthesis and consistency regularization, dubbed HisynSeg. Specifically, synthesized histopathological images with pixel-level masks are generated for fully-supervised model training, where two synthesis strategies are proposed based on Mosaic transformation and Bézier mask generation. Besides, an image filtering module is developed to guarantee the authenticity of the synthesized images. In order to further avoid the model overfitting to the occasional synthesis artifacts, we additionally propose a novel self-supervised consistency regularization, which enables the real images without segmentation masks to supervise the training of the segmentation model. By integrating the proposed techniques, the HisynSeg framework successfully transforms the weakly-supervised semantic segmentation problem into a fully-supervised one, greatly improving the segmentation accuracy. Experimental results on three datasets prove that the proposed method achieves a state-of-the-art performance. Code is available at https://github.com/Vison307/HisynSeg.

Immunohistochemistry (IHC) examination is essential to determine the tumour subtypes, provide key prognostic factors, and develop personalized treatment plans for breast cancer. However, compared to Hematoxylin and Eosin (H&E) staining, the preparation process of IHC staining is more complex and expensive, which limits its application in clinical practice. Therefore, H&E to IHC stain transfer may be an ideal solution to obtain IHC staining. To ensure high transferring quality, it would be much more desirable to exploit the supervised information between adjacent layer images of the same tissue, which are stained by H&E and IHC stainings, respectively. Nevertheless, adjacent layer tissue images are not accurately paired at the pixel level, which poses significant challenges to network training. To address this problem, we propose a generative adversarial network for breast IHC virtual staining, which contains an optimal transport-based supervised information mining (OT-SIM) mechanism and a pathological correlation-based supervised information mining (PC-SIM) mechanism. The OT-SIM guides the network in mining matching consistency between H&E images and the adjacent layer's real IHC images, providing as much instance-level supervision as possible. The PC-SIM further explores the consistency between the correlation among virtual IHC images and the correlation among real IHC images, providing batch-level supervision. Extensive experiments show the superiority of our method on two breast tissue benchmark datasets compared to the state-of-the-art methods both quantitatively and qualitatively. The code is available at https://github.com/xianchaoguan/SIM-GAN.

In clinical practice, frozen section (FS) images can be utilized to obtain the immediate pathological results of the patients in operation due to their fast production speed. However, compared with the formalin-fixed and paraffin-embedded (FFPE) images, the FS images greatly suffer from poor quality. Thus, it is of great significance to transfer the FS image to the FFPE one, which enables pathologists to observe high-quality images in operation. However, obtaining the paired FS and FFPE images is quite hard, so it is difficult to obtain accurate results using supervised methods. Apart from this, the FS to FFPE stain transfer faces many challenges. Firstly, the number and position of nuclei scattered throughout the image are hard to maintain during the transfer process. Secondly, transferring the blurry FS images to the clear FFPE ones is quite challenging. Thirdly, compared with the center regions of each patch, the edge regions are harder to transfer. To overcome these problems, a multi-perspective self-supervised GAN, incorporating three auxiliary tasks, is proposed to improve the performance of FS to FFPE stain transfer. Concretely, a nucleus consistency constraint is designed to enable the high-fidelity of nuclei, an FFPE guided image deblurring is proposed for improving the clarity, and a multi-field-of-view consistency constraint is designed to better generate the edge regions. Objective indicators and pathologists' evaluation for experiments on the five datasets across different countries have demonstrated the effectiveness of our method. In addition, the validation in the downstream task of microsatellite instability prediction has also proved the performance improvement by transferring the FS images to FFPE ones. Our code link is https://github.com/linyiyang98/Self-Supervised-FS2FFPE.git.

As attitude and motion sensing components, inertial sensors are widely used in various portable devices, covering consumer electronics, sports health, aerospace, etc. But the severe intrinsic errors of inertial sensors heavily restrain their function implementation, especially the advanced functionality, including motion trajectory recovery and motion semantic recognition, which attracts considerable attention. As a mainstream signal processing method, wavelet is hailed as the mathematical microscope of signal due to the plentiful and diverse wavelet basis functions. However, complicated noise types and application scenarios of inertial sensors make selecting wavelet basis perplexing. To this end, we propose a wavelet dynamic selection network (WDSNet), which intelligently selects the appropriate wavelet basis for variable inertial signals. In addition, existing deep learning architectures excel at extracting features from input data but neglect to learn the characteristics of target categories, which is essential to enhance the category awareness capability, thereby improving the selection of wavelet basis. Therefore, we propose a category representation mechanism (CRM), which enables the network to extract and represent category features without increasing trainable parameters. Furthermore, CRM transforms the common fully connected network into category representations, which provide closer supervision to the feature extractor than the far and trivial one-hot classification labels. We call this process of imposing interpretability on a network and using it to supervise the feature extractor the feature supervision mechanism, and its effectiveness is demonstrated experimentally and theoretically in this paper. The enhanced inertial signal can perform impracticable tasks with regard to the original signal, such as trajectory reconstruction. Both quantitative and visual results show that WDSNet outperforms the existing methods. Remarkably, WDSNet, as a weakly-supervised method, achieves the state-of-the-art performance of all the compared fully-supervised methods.

Recently, pathological diagnosis has achieved superior performance by combining deep learning models with the multiple instance learning (MIL) framework using whole slide images (WSIs). However, the giga-pixeled nature of WSIs poses a great challenge for efficient MIL. Existing studies either do not consider global dependencies among instances, or use approximations such as linear attentions to model the pair-to-pair instance interactions, which inevitably brings performance bottlenecks. To tackle this challenge, we propose a framework named MamMIL for WSI analysis by cooperating the selective structured state space model (i.e., Mamba) with MIL, enabling the modeling of global instance dependencies while maintaining linear complexity. Specifically, considering the irregularity of the tissue regions in WSIs, we represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we further propose a topology-aware scanning mechanism to serialize the WSI graphs while preserving the topological relationships among the instances. Finally, in order to further perceive the topological structures among the instances and incorporate short-range feature interactions, we propose an instance aggregation block based on graph neural networks. Experiments show that MamMIL can achieve advanced performance than the state-of-the-art frameworks. The code can be accessed at https://github.com/Vison307/MamMIL.

Automatic polyp segmentation in endoscopic images is critical for the early diagnosis of colorectal cancer. Despite the availability of powerful segmentation models, two challenges still impede the accuracy of polyp segmentation algorithms. Firstly, during a colonoscopy, physicians frequently adjust the orientation of the colonoscope tip to capture underlying lesions, resulting in viewpoint changes in the colonoscopy images. These variations increase the diversity of polyp visual appearance, posing a challenge for learning robust polyp features. Secondly, polyps often exhibit properties similar to the surrounding tissues, leading to indistinct polyp boundaries. To address these problems, we propose a viewpoint-aware framework named VANet for precise polyp segmentation. In VANet, polyps are emphasized as a discriminative feature and thus can be localized by class activation maps in a viewpoint classification process. With these polyp locations, we design a viewpoint-aware Transformer (VAFormer) to alleviate the erosion of attention by the surrounding tissues, thereby inducing better polyp representations. Additionally, to enhance the polyp boundary perception of the network, we develop a boundary-aware Transformer (BAFormer) to encourage self-attention towards uncertain regions. As a consequence, the combination of the two modules is capable of calibrating predictions and significantly improving polyp segmentation performance. Extensive experiments on seven public datasets across six metrics demonstrate the state-of-the-art results of our method, and VANet can handle colonoscopy images in real-world scenarios effectively. The source code is available at https://github.com/1024803482/Viewpoint-Aware-Network.

Inertial sensors, as motion-sensing components, are widely used in inertial navigation, aerospace, and consumer electronics. Their wide applications and severe errors form a sharp contradiction, which attracts considerable attention. Wavelet is hailed as the mathematical signal microscope due to the diverse wavelet basis functions. However, complicated noise types and application scenarios of inertial sensors make selecting wavelet basis perplexing. To this end, we propose a wavelet encoding network (WENet), which intelligently selects the appropriate wavelet for variable inertial signals by representing wavelet characteristics through the devised category representation mechanism (CRM). Furthermore, CRM introduces a feature supervision effect, which imposes interpretability on a black-box network and forces it to provide direct supervision for other network structures. This supervision strategy is closer and more effective than the supervision provided by the output end, which is far and needs to go through backpropagation. The proposed WENet has the reliability of the model-driven method and the flexibility of the data-driven method. As a weakly supervised method, the WENet achieves the best performance among all signal improvement methods, including fully supervised ones. After being enhanced by WENet, the low-cost sensor signal can perform accurate spatial trajectory reconstruction, which was once considered an impossible task.

In histopathology, the tissue slides are usually stained by common H&E stain or special stains (MAS, PAS, and PASM, etc.) to clearly show specific tissue structures. The rapid development of deep learning provides a good solution to generate virtual staining images to significantly reduce the time and labor costs associated with histochemical staining. However, most existing methods need to train a special model for every two stains, which consumes a lot of computing resources with the increasing of staining types. To address this problem, we propose an unsupervised multi-domain stain transfer method, GramGAN, which realizes the progressive transfer through cascaded Style-Guided blocks. For each Style-Guided block, we design a style encoding dictionary to characterize and store all the staining style information. In addition, we propose a Rényi entropy-based regularization term to improve the discrimination ability of different styles. The experimental results show that our method can realize accurate transferring among multiple staining styles with better performance. Furthermore, we build and publish a special stained image dataset suitable for glomeruli segmentation (including H&E staining), where the accuracy of glomeruli detection and segmentation can be significantly improved after transferring H&E-stained images to PAS-stained and PASM-stained ones by our method. The code is publicly available at: https://github.com/xianchaoguan/GramGAN.

Tissue segmentation is a critical task in computational pathology due to its desirable ability to indicate the prognosis of cancer patients. Currently, numerous studies attempt to use image-level labels to achieve pixel-level segmentation to reduce the need for fine annotations. However, most of these methods are based on class activation map, which suffers from inaccurate segmentation boundaries. To address this problem, we propose a novel weakly-supervised tissue segmentation framework named PistoSeg, which is implemented under a fully-supervised manner by transferring tissue category labels to pixel-level masks. Firstly, a dataset synthesis method is proposed based on Mosaic transformation to generate synthesized images with pixel-level masks. Next, considering the difference between synthesized and real images, this paper devises an attention-based feature consistency, which directs the training process of a proposed pseudo-mask refining module. Finally, the refined pseudo-masks are used to train a precise segmentation model for testing. Experiments based on WSSS4LUAD and BCSS-WSSS validate that PistoSeg outperforms the state-of-the-art methods. The code is released at https://github.com/Vison307/PistoSeg.

Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis analysis are promising directions in computational pathology. However, limited by expensive and time-consuming annotation costs, WSIs usually only have weak annotations, including 1) WSI-level Annotations (WA) and 2) Limited Patch-level Annotations (LPA). Currently, Multiple Instance Learning (MIL) often exploits WA, while LPA usually assign pseudo-labels for unlabeled data. Intuitively, pseudo-labels can serve as a practical guide for MIL, but the unreliable prediction caused by LPA inevitably introduce noise. Furthermore, WA-supervised MIL training inevitably suffers from the semantical unalignment between instances and bag-level labels. To address these problems, we design a framework called Learning from Noisy Pseudo Labels for promoting Multiple Instance Learning (LNPL-MIL), which considers both types of weak annotation. Specifically, for the LPA-trained weak classifier, we design a Super-Patch-based LNPL (SP-LNPL) method to reduce false positives in the noisy pseudo-labels and then select more accurate Top-K key instances. In MIL, we propose a Transformer aware of instance Order and Distribution (TOD-MIL) that strengthens instances correlation and weakens semantical unalignment in the bag. We validate our LNPL-MIL on Tumor Diagnosis and Survival Prediction, achieving state-of-the-art performance with at least 2.7%/2.9% AUC and 2.6%/2.3% C-Index improvement with the patches labeled for two scale. Ablation study and visualization analysis further verify the effectiveness.

Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and prognosis. However, it is very challenging to achieve satisfactory performance in LNM classification, because both the morphology and spatial distribution of tumor regions should be taken into account. To address this problem, this article proposes a two-stage dMIL-Transformer framework, which integrates both the morphological and spatial information of the tumor regions based on the theory of multiple instance learning (MIL). In the first stage, a double Max-Min MIL (dMIL) strategy is devised to select the suspected top-K positive instances from each input histopathology image, which contains tens of thousands of patches (primarily negative). The dMIL strategy enables a better decision boundary for selecting the critical instances compared with other methods. In the second stage, a Transformer-based MIL aggregator is designed to integrate all the morphological and spatial information of the selected instances from the first stage. The self-attention mechanism is further employed to characterize the correlation between different instances and learn the bag-level representation for predicting the LNM category. The proposed dMIL-Transformer can effectively deal with the thorny classification in LNM with great visualization and interpretability. We conduct various experiments over three LNM datasets, and achieve 1.79%-7.50% performance improvement compared with other state-of-the-art methods.

As an essential step in the pathological diagnosis, histochemical staining can show specific tissue structure information and, consequently, assist pathologists in making accurate diagnoses. Clinical kidney histopathological analyses usually employ more than one type of staining: H&E, MAS, PAS, PASM, etc. However, due to the interference of colors among multiple stains, it is not easy to perform multiple staining simultaneously on one biological tissue. To address this problem, we propose a network based on unpaired training data to virtually generate multiple types of staining from one staining. Our method can preserve the content of input images while transferring them to multiple target styles accurately. To efficiently control the direction of stain transfer, we propose a style guided normalization (SGN). Furthermore, a multiple style encoding (MSE) is devised to represent the relationship among different staining styles dynamically. An improved one-hot label is also proposed to enhance the generalization ability and extendibility of our method. Vast experiments have demonstrated that our model can achieve superior performance on a tiny dataset. The results exhibit not only good performance but also great visualization and interpretability. Especially, our method also achieves satisfactory results over cross-tissue, cross-staining as well as cross-task. We believe that our method will significantly influence clinical stain transfer and reduce the workload greatly for pathologists. Our code and Supplementary materials are available at https://github.com/linyiyang98/UMDST.

Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. However, the current MIL methods are usually based on independent and identical distribution hypothesis, thus neglect the correlation among different instances. To address this problem, we proposed a new framework, called correlated MIL, and provided a proof for convergence. Based on this framework, we devised a Transformer based MIL (TransMIL), which explored both morphological and spatial information. The proposed TransMIL can effectively deal with unbalanced/balanced and binary/multiple classification with great visualization and interpretability. We conducted various experiments for three different computational pathology problems and achieved better performance and faster convergence compared with state-of-the-art methods. The test AUC for the binary tumor classification can be up to 93.09% over CAMELYON16 dataset. And the AUC over the cancer subtypes classification can be up to 96.03% and 98.82% over TCGA-NSCLC dataset and TCGA-RCC dataset, respectively. Implementation is available at: https://github.com/szc19990412/TransMIL.