Pathological grading of laryngeal squamous cell carcinoma (LSCC) based on whole-slide image (WSI) is crucial for the diagnosis, treatment and prognosis. According to pathologists' knowledge, tumor regions are highly associated with grading. However, existing multiple instance learning (MIL) methods tend to overrepresent weakly relevant non-tumor regions and irrelevant background, leading to poor grading performance and interpretability. Motivated by the above problems, we propose an end-to-end knowledge-driven MIL network with hierarchical cluster-incorporated aware filtering, i.e. HCF-MIL. Firstly, we develop the tumor-guiding cluster filtering for feature representation, which awarely filters out irrelevant instance-level information and adaptively assigns learnable weights to tumor and non-tumor instances. Secondly, conventional mean-based and max-based aggregation primarily capture the overall patterns, neglecting the contributions of the most representative individual instances. Therefore, we propose a novel enhanced filtering aggregation learning strategy to strengthen hierarchical tumor-related feature representation. Through end-to-end optimization, HCF-MIL reduces model's entropy value and facilitates better alignment between decision-making process and diagnostic behaviors of pathologists. Experiments on larynx and multicentre datasets show that HCF-MIL significantly improves both pathological grading performance and interpretability, providing a strong foundation for reliable clinical deployment.

Segmenting programmed cell death-ligand 1 (PD-L1) expression regions in lung squamous cell carcinoma from pathological H&E images represents a challenging pixel-level prediction task, attributed to the morphological heterogeneity and size discrepancies of expression areas. Although hybrid architectures of CNN and Transformer can extract local features and capture long-range dependencies, they inadequately address information interaction and redundant information elimination during the fusion process, adversely impacting PD-L1 segmentation accuracy. To address this, we propose a W-shaped dual-encoder network (DEW-Net) with novel attention fusion mechanisms. First, a CNN encoder and a Swin Transformer encoder are connected in parallel to extract multi-layer local and global features from pathological images, respectively. Second, a Cross-Attention Fusion (CAF) module is proposed to strengthen information interaction and semantic feature fusion. Additionally, a Channel Attention (CA) is introduced in skip connections to enhance the channel-wise information of shallow features, while a Bilateral-voting Position Attention (BPA) module is further proposed to eliminate positional noise in same-scale shallow features and reinforce position-wise information. We conducted extensive experiments on four datasets. On the PD-L1 segmentation dataset, DEW-Net achieved superior performance, with DSC and IoU reaching 79.93% and 71.27%, respectively. These results demonstrate its strong performance and generalization capability compared to other state-of-the-art (SOTA) methods.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

The tumor grading of laryngeal cancer pathological images needs to be accurate and interpretable. The deep learning model based on the attention mechanism-integrated convolution (AMC) block has good inductive bias capability but poor interpretability, whereas the deep learning model based on the vision transformer (ViT) block has good interpretability but weak inductive bias ability. Therefore, we propose an end-to-end ViT-AMC network (ViT-AMCNet) with adaptive model fusion and multiobjective optimization that integrates and fuses the ViT and AMC blocks. However, existing model fusion methods often have negative fusion: 1). There is no guarantee that the ViT and AMC blocks will simultaneously have good feature representation capability. 2). The difference in feature representations learning between the ViT and AMC blocks is not obvious, so there is much redundant information in the two feature representations. Accordingly, we first prove the feasibility of fusing the ViT and AMC blocks based on Hoeffding's inequality. Then, we propose a multiobjective optimization method to solve the problem that ViT and AMC blocks cannot simultaneously have good feature representation. Finally, an adaptive model fusion method integrating the metrics block and the fusion block is proposed to increase the differences between feature representations and improve the deredundancy capability. Our methods improve the fusion ability of ViT-AMCNet, and experimental results demonstrate that ViT-AMCNet significantly outperforms state-of-the-art methods. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and the generalization ability is also excellent. Our code is publicly available at https://github.com/Baron-Huang/ViT-AMCNet.

Cervical cancer develops in the lower part of the uterus, the organ of the female apparatus where the embryo is received and develops during pregnancy. In this paper we investigate the possibility to automatically detect the presence of cancerous cells and to predict of the stage of the cancerous lesion of the uterine cervix by exploiting images of cervical cells captured by the microscope. We extract a set of numerical features from each images and we build supervised machine learning models to diagnose the cervix cancer. The experimental analysis show that the proposed method is promising in distinguish between healthy and cancerous cells and to detect also high and low-grade squamous intraepithelial lesions.

Laryngeal cancer tumor (LCT) grading is a challenging task in P63 Immunohistochemical (IHC) histopathology images due to small differences between LCT levels in pathology images, the lack of precision in lesion regions of interest (LROIs) and the paucity of LCT pathology image samples. The key to solving the LCT grading problem is to transfer knowledge from other images and to identify more accurate LROIs, but the following problems occur: 1) transferring knowledge without a priori experience often causes negative transfer and creates a heavy workload due to the abundance of image types, and 2) convolutional neural networks (CNNs) constructing deep models by stacking cannot sufficiently identify LROIs, often deviate significantly from the LROIs focused on by experienced pathologists, and are prone to providing misleading second opinions. So we propose a novel fusion attention block network (FABNet) to address these problems. First, we propose a model transfer method based on clinical a priori experience and sample analysis (CPESA) that analyzes the transfer ability by integrating clinical a priori experience using indicators such as the relationship between the cancer onset location and morphology and the texture and staining degree of cell nuclei in histopathology images; our method further validates these indicators by the probability distribution of cancer image samples. Then, we propose a fusion attention block (FAB) structure, which can both provide an advanced non-uniform sparse representation of images and extract spatial relationship information between nuclei; consequently, the LROI can be more accurate and more relevant to pathologists. We conducted extensive experiments, compared with the best Baseline model, the classification accuracy is improved 25%, and It is demonstrated that FABNet performs better on different cancer pathology image datasets and outperforms other state of the art (SOTA) models.