Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods.

Multimodal survival prediction combining genomic features with histopathology whole slide images (WSIs) has shown promise, but genomic data remains costly and inaccessible for widespread clinical adoption. In contrast, clinical data and multi-stained WSIs are routinely available. We present Contrastive Learning guided by Pathological Reports (CLPR), a multimodal survival prediction framework that integrates H&Estained WSIs, immunohistochemical (IHC)-stained WSIs, and structured pathological reports. Unlike genomic approaches, IHC provides protein-level tumor microenvironment information at lower cost, while pathological reports bridge clinical diagnosis with prognostic assessment. CLPR addresses key challenges in multimodal fusion through three innovations: (1) pathological report-guided contrastive learning that identifies semantically meaningful sample pairs for robust cross-modal alignment, overcoming unreliable attention scores under weak supervision; (2) a distribution-aware regularization constraint that explicitly preserves inter-group separability and intra-group compactness between risk cohorts, enhancing discriminative power; and (3) the first multimodal survival dataset combining H&E- and IHC-stained WSIs with structured pathological reports. On our clinical gastric cancer dataset (270 patients), CLPR achieves a C-index of 0.785 with significant risk stratification (log-rank p$C$-index: 0.772), demonstrating clinical versatility.

Camouflaged object detection (COD) is challenging for both human and computer vision, as targets often blend into the background by sharing similar color, texture, or shape. While many feature enhancement techniques exist, single-view methods tend to overemphasize certain Recognizing that camouflaged objects exhibit different concealment strategies under varying observational perspectives, we propose HUNTNet, a network that establishes a dynamic detection mechanism to decouple target features from RGB images and perform topological decamouflage across multiple homomorphic feature spaces through a unified feature focusing architecture. We adopt PVTv2 as the backbone to extract multi-perspective spatial features. Detail representation is enhanced via a feature module that integrates Dual-Channel Recursive (DCR), Wavelet-Gabor Transform (WGT), and Anisotropic Gradient Responding (AGR), which together improve boundary discrimination and edge contour detection. To further boost performance, the Simplicial Feature Integration (SFI) module recursively fuses multi-layer features, enabling high-resolution focus on target regions. Experiments show that HUNTNet surpasses state-of-the-art methods in both accuracy and generalization, offering a robust solution for COD and improving segmentation in complex scenes. Our code is available at https://github.com/HaolinJi817/HUNTNet.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel partial-label contrastive representation learning approach to enhance the discrimination of histopathology image representations for fine-grained biomarkers prediction. We designed a partial-label contrastive clustering (PLCC) module for partial-label disambiguation and a dynamic clustering algorithm to sample the most representative features of each category to the clustering queue during the contrastive learning process. We conducted comprehensive experiments on three gene mutation prediction datasets, including USTC-EGFR, BRCA-HER2, and TCGA-EGFR. The results show that our method outperforms 9 existing methods in terms of Accuracy, AUC, and F1 Score. Specifically, our method achieved an AUC of 0.950 in EGFR mutation subtyping of TCGA-EGFR and an AUC of 0.853 in HER2 0/1+/2+/3+ grading of BRCA-HER2, which demonstrates its superiority in fine-grained biomarkers prediction from histopathology whole slide images.

The development of computational pathology lies in the consensus that pathological characteristics of tumors are significant guidance for cancer diagnostics. Most existing research focuses on the inner-contextual information within each WSI yet ignores the possible inter-correlations between slides. As the development of tumors is a continuous process involving a series of histological, morphological, and genetic changes that accumulate over time, the similarities and differences between WSIs across various stages, grades, locations and patients should potentially contribute to the representation of WSIs and deserve to be taken into account in WSI modeling. To verify the advancement of introducing the slide inter-correlations into the representation learning of WSIs, we proposed a generic WSI analysis pipeline SlideGCD that can be adapted to any existing Multiple Instance Learning (MIL) frameworks and improve their performance. With the new paradigm, the prior knowledge of cancer development can participate in the end-to-end workflow, which concurrently initializes and refines the slide representation, as a guide for message passing in the slide-based graph. Extensive comparisons and experiments are conducted to validate the effectiveness and robustness of the proposed pipeline across 4 different tasks, including cancer subtyping, cancer staging, survival prediction, and gene mutation prediction, with 8 representative SOTA WSI analysis frameworks as backbones. The code is available at https://github.com/HFUT-miaLab/SlideGCD.

Multiple instance learning (MIL) based whole slide image (WSI) classification is often carried out on the representations of patches extracted from WSI with a pre-trained patch encoder. The performance of classification relies on both patch-level representation learning and MIL classifier training. Most MIL methods utilize a frozen model pre-trained on ImageNet or a model trained with self-supervised learning on histopathology image dataset to extract patch image representations and then fix these representations in the training of the MIL classifiers for efficiency consideration. However, the invariance of representations cannot meet the diversity requirement for training a robust MIL classifier, which has significantly limited the performance of the WSI classification. In this paper, we propose a Self-Supervised Representation Distribution Learning framework (SSRDL) for patch-level representation learning with an online representation sampling strategy (ORS) for both patch feature extraction and WSI-level data augmentation. The proposed method was evaluated on three datasets under three MIL frameworks. The experimental results have demonstrated that the proposed method achieves the best performance in histopathology image representation learning and data augmentation and outperforms state-of-the-art methods under different WSI classification frameworks. The code is available at https://github.com/lazytkm/SSRDL.

Large-scale pre-training models have promoted the development of histopathology image analysis. However, existing self-supervised methods for histopathology images primarily focus on learning patch features, while there is a notable gap in the availability of pre-training models specifically designed for WSI-level feature learning. In this paper, we propose a novel self-supervised learning framework for pan-cancer WSI-level representation pre-training with the designed position-aware masked autoencoder (PAMA). Meanwhile, we propose the position-aware cross-attention (PACA) module with a kernel reorientation (KRO) strategy and an anchor dropout (AD) mechanism. The KRO strategy can capture the complete semantic structure and eliminate ambiguity in WSIs, and the AD contributes to enhancing the robustness and generalization of the model. We evaluated our method on 7 large-scale datasets from multiple organs for pan-cancer classification tasks. The results have demonstrated the effectiveness and generalization of PAMA in discriminative WSI representation learning and pan-cancer WSI pre-training. The proposed method was also compared with 8 WSI analysis methods. The experimental results have indicated that our proposed PAMA is superior to the state-of-the-art methods. The code and checkpoints are available at https://github.com/WkEEn/PAMA.

Traditional PCR/NGS-based multigene panel testing is time-consuming and costly. Predicting EGFR mutations directly from H&E stained whole slide images (WSIs) can alleviate these limitations. Furthermore, histopathological reports contain valuable textual information that correlates with tissue areas in WSIs. However, recent research mainly analyses EGFR mutation status only from a single modality, ignoring rich information contained in reports. In this paper, we propose a report-guided cross-modal representation learning method for predicting EGFR mutations by WSIs. Specifically, we reconstruct report-level embeddings through exploring intrinsic relationships between diagnostic words in histopathological reports and tissue areas in WSIs. Finally, reconstructed histopathological report embedding and aggregated WSI embedding are fused for final prediction. More importantly, molecular testing report is also introduced as prior supervision information at the training stage to guarantee semantic consistency of fused feature and molecular report embedding. We evaluate our method on the TCGA-EGFR public benchmark dataset and an in-house clinical dataset (USTC-EGFR). Experimental results demonstrate that our method outperforms existing approaches in EGFR mutation prediction, highlighting the benefits of cross-modal learning in enhancing feature representational ability. The code is available at https://github.com/HFUT-miaLab/RCRL.

Transformer has been widely used in histopathology whole slide image analysis. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits its effectiveness and efficiency when applied to gigapixel histopathology images. In this paper, we propose a novel kernel attention Transformer (KAT) for histopathology WSI analysis and assistant cancer diagnosis. The information transmission in KAT is achieved by cross-attention between the patch features and a set of kernels related to the spatial relationship of the patches on the whole slide images. Compared to the common Transformer structure, KAT can extract the hierarchical context information of the local regions of the WSI and provide diversified diagnosis information. Meanwhile, the kernel-based cross-attention paradigm significantly reduces the computational amount. The proposed method was evaluated on three large-scale datasets and was compared with 8 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI analysis and is superior to the state-of-the-art methods.

Computer-aided cancer diagnosis (CAD) methods based on the histopathological images have achieved great development. The content-based whole slide image (WSI) retrieval is one of the important application that can search for the informative data to assist clinical diagnosis. It is notable that the current retrieval system are mainly developed based on the image content and image labels. The diagnosis report for the WSIs given by the pathologists are also valuable data, but have not yet been adequately considered in modeling. In this paper, we propose a cross-modal retrieval framework based on histopathology WSIs and diagnosis report, which can simultaneously achieve four retrieval tasks for histopathology database across WSIs and diagnosis reports. The compact binary features from both WSIs and diagnosis reports are first extracted, and then built in a common vision-language semantic feature space by the constraint of the designed cross hashing loss function. The method was verified on a gastric histopathology dataset that contains 932 gastric cases with 4 lesion categories. Experimental results have demonstrated the effectiveness of the proposed method in the cross-modal retrieval tasks for digital pathology system.

As the successor of H.265/HEVC, the new versatile video coding standard (H.266/VVC) can provide up to 50% bitrate saving with the same subjective quality, at the cost of increased decoding complexity. To accelerate the application of the new coding standard, a real-time H.266/VVC software decoder that can support various platforms is implemented, where SIMD technologies, parallelism optimization, and the acceleration strategies based on the characteristics of each coding tool are applied. As the mobile devices have become an essential carrier for video services nowadays, the mentioned optimization efforts are not only implemented for the x86 platform, but more importantly utilized to highly optimize the decoding performance on the ARM platform in this work. The experimental results show that when running on the Apple A14 SoC (iPhone 12pro), the average single-thread decoding speed of the present implementation can achieve 53fps (RA and LB) for full HD (1080p) bitstreams generated by VTM-11.0 reference software using 8bit Common Test Conditions (CTC). When multi-threading is enabled, an average of 32 fps (RA) can be achieved when decoding the 4K bitstreams.

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354 ±0.1149 to 0.8372 ±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 ±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods.

The development of whole slide imaging techniques and online digital pathology platforms have accelerated the popularization of telepathology for remote tumor diagnoses. During a diagnosis, the behavior information of the pathologist can be recorded by the platform and then archived with the digital case. The browsing path of the pathologist on the WSI is one of the valuable information in the digital database because the image content within the path is expected to be highly correlated with the diagnosis report of the pathologist. In this article, we proposed a novel approach for computer-assisted cancer diagnosis named session-based histopathology image recommendation (SHIR) based on the browsing paths on WSIs. To achieve the SHIR, we developed a novel diagnostic regions attention network (DRA-Net) to learn the pathology knowledge from the image content associated with the browsing paths. The DRA-Net does not rely on the pixel-level or region-level annotations of pathologists. All the data for training can be automatically collected by the digital pathology platform without interrupting the pathologists' diagnoses. The proposed approaches were evaluated on a gastric dataset containing 983 cases within 5 categories of gastric lesions. The quantitative and qualitative assessments on the dataset have demonstrated the proposed SHIR framework with the novel DRA-Net is effective in recommending diagnostically relevant cases for auxiliary diagnosis. The MRR and MAP for the recommendation are respectively 0.816 and 0.836 on the gastric dataset. The source code of the DRA-Net is available at https://github.com/zhengyushan/dpathnet.

Screen content video applications require efficient coding of computer-generated materials. The new screen content coding tools such as intra block copy (IBC) and palette mode (PLT) have addressed this requirement. However, the added computational complexity on top of the existing sophisticated video encoders is also challenging. In this paper, we focus on the fast and efficient encoder implementation of these screen content coding tools. Improvements on hash-based IBC search, PLT optimization, mode decision between PLT and intra mode, and other general encoder accelerations towards screen content applications are studied and discussed. Experimental results show that with these methods added, the encoder can achieve some faster runtime performance than before while the compression efficiency is almost doubled with screen content coding tools.

In the High Efficiency Video Coding (HEVC), the predicted pixels generated by the intra angular prediction are the same along the prediction direction. In this paper, an improved gradient based interpolation for intra angular prediction is proposed. The gradient is generated by both row and column reference samples according to the prediction direction and changed dynamically for each pixel. It is appended to the original intra prediction process to improve the performance of interpolation. This method describes the features of directional gradient changes, which improves the performance of intra interpolation. The method achieves up to 1.90% BD-Rate reduction and ignorable decoding time increase under intra main configuration based on HM 16.7.

Content-based image retrieval (CBIR) has been widely researched for histopathological images. It is challenging to retrieve contently similar regions from histopathological whole slide images (WSIs) for regions of interest (ROIs) in different size. In this paper, we propose a novel CBIR framework for database that consists of WSIs and size-scalable query ROIs. Each WSI in the database is encoded into a matrix of binary codes. When retrieving, a group of region proposals that have similar size with the query ROI are firstly located in the database through an efficient table-lookup approach. Then, these regions are ranked by a designed multi-binary-code-based similarity measurement. Finally, the top relevant regions and their locations in the WSIs as well as the corresponding diagnostic information are returned to assist pathologists. The effectiveness of the proposed framework is evaluated on a fine-annotated WSI database of epithelial breast tumors. The experimental results have proved that the proposed framework is effective for retrieval from database that consists of WSIs. Specifically, for query ROIs of 4096 4096 pixels, the retrieval precision of the top 20 return has reached 96% and the retrieval time is less than 1.5 s.

Histopathological image classification (HIC) and content-based histopathological image retrieval (CBHIR) are two promising applications for the histopathological whole slide image (WSI) analysis. HIC can efficiently predict the type of lesion involved in a histopathological image. In general, HIC can aid pathologists in locating high-risk cancer regions from a WSI by providing a cancerous probability map for the WSI. In contrast, CBHIR was developed to allow searches for regions with similar content for a region of interest (ROI) from a database consisting of historical cases. Sets of cases with similar content are accessible to pathologists, which can provide more valuable references for diagnosis. A drawback of the recent CBHIR framework is that a query ROI needs to be manually selected from a WSI. An automatic CBHIR approach for a WSI-wise analysis needs to be developed. In this paper, we propose a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR. In our framework, CBHIR is automatically processed throughout the WSI, based on which a probability map regarding the malignancy of breast tumors is calculated. Through the probability map, the malignant regions in WSIs can be easily recognized. Furthermore, the retrieval results corresponding to each sub-region of the WSIs are recorded during the automatic analysis and are available to pathologists during their diagnosis. Our method was validated on fully annotated WSI data sets of breast tumors. The experimental results certify the effectiveness of the proposed method.

In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.

Content-based image retrieval (CBIR) for digital pathology slides is of clinical use for breast cancer aided diagnosis. One of the largest challenges in CBIR is feature extraction. In this paper, we propose a novel pathology image retrieval method for breast cancer, which aims to characterize the pathology image content through texture and pathological features and further discover the latent high-level semantics. Specifically, the proposed method utilizes block Gabor features to describe the texture structure, and simultaneously designs nucleus-based pathological features to describe morphological characteristics of nuclei. Based on these two kinds of local feature descriptors, two codebooks are built to learn the probabilistic latent semantic analysis (pLSA) models. Consequently, each image is represented by the topics of pLSA models which can reveal the semantic concepts. Experimental results on the digital pathology image database for breast cancer demonstrate the feasibility and effectiveness of our method.

This work focuses on the circuit and system implementation of a magnetic immunoassay based microsystem platform to be used as sensor terminal for detecting protein toxins in environment. Three main challenges facing this work-the design of a high performance sensor, the packaging technique and the design of integrated circuit are introduced. Planar microcoil array fabricated both on silicon substrate and polymer substrate are exploited as sensor of magnetic particles, whereas microchannel and ultra thin bottom microplate for traditional ELISA was used for reagents, correspondingly. Simulation results of two detection circuits prove that our system is able to detect magnetic particles in different volumes, thus the proposed microsystem has potential for medical diagnostics, food pathogen detection or water analysis.

We propose in this paper a portable lab-on-chip (LoC) platform dedicated for magnetic beads density measuring. The device consists of two main parts: disposable microfluidic structure and reusable electronic part. With results displayed on build-in LCD screen, the conventional bulky observation equipment is avoided. The principle of detection is that the presence of magnetic beads can affect the effective inductance of planar microcoil integrated inside of the LoC platform. In order to read out the inductance variation, we proposed a CMOS 0.18um application specific integrated circuit, which includes two different sensing blocks, namely impedance sensing and frequency sensing circuit. Preliminary experimental results with magnetic beads show that our proposed LoC platform allows measuring the density of magnetic beads in a wide range with fine linearity, either in continuous-flow microfluidics or digital mcirofluidics.

In some magnetic beads based lab-on-chip (LoC) applications, such as purification and fast cell detection, high throughput capacity is required. In this paper, we propose an optimization method for the implementation of in-channel planar microcoils array. By generating more dispersed trapping centers and exploiting the array scanning scheme, the problem of interaction among magnetic beads is controlled and both power consumption and Joule heating are reduced. Simulation results by Finite Element Analysis software show that under the first order optimization, the proposed topology saves 69% power, while keeps approximate total trapping area, compared with the conventional topologies. The microfluidic structure combining the proposed coils array and operation scheme is suitable for high throughput LoC applications.