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.

In this paper, we propose a novel method, namely GR-PSN, which learns surface normals from photometric stereo images and generates the photometric images under distant illumination from different lighting directions and surface materials. The framework is composed of two subnetworks, named GeometryNet and ReconstructNet, which are cascaded to perform shape reconstruction and image rendering in an end-to-end manner. ReconstructNet introduces additional supervision for surface-normal recovery, forming a closed-loop structure with GeometryNet. We also encode lighting and surface reflectance in ReconstructNet, to achieve arbitrary rendering. In training, we set up a parallel framework to simultaneously learn two arbitrary materials for an object, providing an additional transform loss. Therefore, our method is trained based on the supervision by three different loss functions, namely the surface-normal loss, reconstruction loss, and transform loss. We alternately input the predicted surface-normal map and the ground-truth into ReconstructNet, to achieve stable training for ReconstructNet. Experiments show that our method can accurately recover the surface normals of an object with an arbitrary number of inputs, and can re-render images of the object with arbitrary surface materials. Extensive experimental results show that our proposed method outperforms those methods based on a single surface recovery network and shows realistic rendering results on 100 different materials.

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.

Survival prediction based on whole slide images (WSIs) is a challenging task for patient-level multiple instance learning (MIL). Due to the vast amount of data for a patient (one or multiple gigapixels WSIs) and the irregularly shaped property of WSI, it is difficult to fully explore spatial, contextual, and hierarchical interaction in the patient-level bag. Many studies adopt random sampling pre-processing strategy and WSI-level aggregation models, which inevitably lose critical prognostic information in the patient-level bag. In this work, we propose a hierarchical vision Transformer framework named HVTSurv, which can encode the local-level relative spatial information, strengthen WSI-level context-aware communication, and establish patient-level hierarchical interaction. Firstly, we design a feature pre-processing strategy, including feature rearrangement and random window masking. Then, we devise three layers to progressively obtain patient-level representation, including a local-level interaction layer adopting Manhattan distance, a WSI-level interaction layer employing spatial shuffle, and a patient-level interaction layer using attention pooling. Moreover, the design of hierarchical network helps the model become more computationally efficient. Finally, we validate HVTSurv with 3,104 patients and 3,752 WSIs across 6 cancer types from The Cancer Genome Atlas (TCGA). The average C-Index is 2.50-11.30% higher than all the prior weakly supervised methods over 6 TCGA datasets. Ablation study and attention visualization further verify the superiority of the proposed HVTSurv. Implementation is available at: https://github.com/szc19990412/HVTSurv.

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.

We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the attention-weighted loss learned in a self-supervise manner for each pixel, avoiding blurry reconstruction result in such difficult regions. The network first estimates a surface normal map and an adaptive attention map, and then the latter is used to calculate a pixel-wise attention-weighted loss that focuses on complex regions. In these regions, the attention-weighted loss applies higher weights of the detail-preserving gradient loss to produce clear surface reconstructions. Experiments on real datasets show that our approach significantly outperforms traditional photometric stereo algorithms and state-of-the-art learning-based methods.