Semi-Automated Quality Assurance in Digital Pathology: Tile Classification Approach

Quality assurance is a critical but underexplored area in digital pathology, where even minor artifacts can have significant effects. Artifacts have been shown to negatively impact the performance of AI diagnostic models. In current practice, trained staff manually review digitized images prior to release of these slides to pathologists which are then used to render a diagnosis. Conventional image processing approaches, provide a foundation for detecting artifacts on digital pathology slides. However, current tools do not leverage deep learning, which has the potential to improve detection accuracy and scalability. Despite these advancements, methods for quality assurance in digital pathology remain limited, presenting a gap for innovation. We propose an AI algorithm designed to screen digital pathology slides by analyzing tiles and categorizing them into one of 10 predefined artifact types or as background. This algorithm identifies and localizes artifacts, creating a map that highlights regions of interest. By directing human operators to specific tiles affected by artifacts, the algorithm minimizes the time and effort required to manually review entire slides for quality issues. From internal archives and The Cancer Genome Atlas, 133 whole slide images were selected and 10 artifacts were annotated using an internally developed software ZAPP (Mayo Clinic, Jacksonville, FL). Ablation study of multiple models at different tile sizes and magnification was performed. InceptionResNet was selected. Single artifact models were trained and tested, followed by a limited multiple instance model with artifacts that performed well together (chatter, fold, and pen). From the results of this study we suggest a hybrid design for artifact screening composed of both single artifact binary models as well as multiple instance models to optimize detection of each artifact.

Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry

Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign from malignant tissue, it involves increased work, higher costs, and diagnostic delays. Artificial intelligence (AI) presents a promising solution to reduce reliance on IHC by accurately classifying atypical glands and borderline morphologies in hematoxylin & eosin (H&E) stained tissue sections. In this study, we evaluated an AI model's ability to minimize IHC use without compromising diagnostic accuracy by retrospectively analyzing prostate core needle biopsies from routine diagnostics at three different pathology sites. These cohorts were composed exclusively of difficult cases where the diagnosing pathologists required IHC to finalize the diagnosis. The AI model demonstrated area under the curve values of 0.951-0.993 for detecting cancer in routine H&E-stained slides. Applying sensitivity-prioritized diagnostic thresholds reduced the need for IHC staining by 44.4%, 42.0%, and 20.7% in the three cohorts investigated, without a single false negative prediction. This AI model shows potential for optimizing IHC use, streamlining decision-making in prostate pathology, and alleviating resource burdens.

Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models

Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving patient survival rates. Machine learning has revolutionized diagnostic methods in recent years by developing classification models that detect diseases based on selected features. However, these classification tasks are often highly imbalanced, limiting the performance of classical models. Quantum models offer a promising alternative, exploiting their ability to express complex patterns by operating in a higher-dimensional computational space through superposition and entanglement. These unique properties make quantum models potentially more effective in addressing the challenges of imbalanced datasets. This work evaluates the potential of quantum classifiers in healthcare, focusing on Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs), comparing them with popular classical models. The study is based on three well-known healthcare datasets -- Prostate Cancer, Heart Failure, and Diabetes. The results indicate that QSVMs outperform QNNs across all datasets due to their susceptibility to overfitting. Furthermore, quantum models prove the ability to overcome classical models in scenarios with high dataset imbalance. Although preliminary, these findings highlight the potential of quantum models in healthcare classification tasks and lead the way for further research in this domain.

A Shape-Aware Total Body Photography System for In-focus Surface Coverage Optimization

Total Body Photography (TBP) is becoming a useful screening tool for patients at high risk for skin cancer. While much progress has been made, existing TBP systems can be further improved for automatic detection and analysis of suspicious skin lesions, which is in part related to the resolution and sharpness of acquired images. This paper proposes a novel shape-aware TBP system automatically capturing full-body images while optimizing image quality in terms of resolution and sharpness over the body surface. The system uses depth and RGB cameras mounted on a 360-degree rotary beam, along with 3D body shape estimation and an in-focus surface optimization method to select the optimal focus distance for each camera pose. This allows for optimizing the focused coverage over the complex 3D geometry of the human body given the calibrated camera poses. We evaluate the effectiveness of the system in capturing high-fidelity body images. The proposed system achieves an average resolution of 0.068 mm/pixel and 0.0566 mm/pixel with approximately 85% and 95% of surface area in-focus, evaluated on simulation data of diverse body shapes and poses as well as a real scan of a mannequin respectively. Furthermore, the proposed shape-aware focus method outperforms existing focus protocols (e.g. auto-focus). We believe the high-fidelity imaging enabled by the proposed system will improve automated skin lesion analysis for skin cancer screening.

A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection

Recent advancements in detecting tumors using deep learning on breast ultrasound images (BUSI) have demonstrated significant success. Deep CNNs and vision-transformers (ViTs) have demonstrated individually promising initial performance. However, challenges related to model complexity and contrast, texture, and tumor morphology variations introduce uncertainties that hinder the effectiveness of current methods. This study introduces a novel hybrid framework, CB-Res-RBCMT, combining customized residual CNNs and new ViT components for detailed BUSI cancer analysis. The proposed RBCMT uses stem convolution blocks with CNN Meet Transformer (CMT) blocks, followed by new Regional and boundary (RB) feature extraction operations for capturing contrast and morphological variations. Moreover, the CMT block incorporates global contextual interactions through multi-head attention, enhancing computational efficiency with a lightweight design. Additionally, the customized inverse residual and stem CNNs within the CMT effectively extract local texture information and handle vanishing gradients. Finally, the new channel-boosted (CB) strategy enriches the feature diversity of the limited dataset by combining the original RBCMT channels with transfer learning-based residual CNN-generated maps. These diverse channels are processed through a spatial attention block for optimal pixel selection, reducing redundancy and improving the discrimination of minor contrast and texture variations. The proposed CB-Res-RBCMT achieves an F1-score of 95.57%, accuracy of 95.63%, sensitivity of 96.42%, and precision of 94.79% on the standard harmonized stringent BUSI dataset, outperforming existing ViT and CNN methods. These results demonstrate the versatility of our integrated CNN-Transformer framework in capturing diverse features and delivering superior performance in BUSI cancer diagnosis.

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models

Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. To address this limitation, we introduce cGSA, a novel AI-driven framework that enhances GSA by incorporating context-aware pathway prioritization. cGSA integrates gene cluster detection, enrichment analysis, and large language models to identify pathways that are not only statistically significant but also biologically meaningful. Benchmarking on 102 manually curated gene sets across 19 diseases and ten disease-related biological mechanisms shows that cGSA outperforms baseline methods by over 30%, with expert validation confirming its increased precision and interpretability. Two independent case studies in melanoma and breast cancer further demonstrate its potential to uncover context-specific insights and support targeted hypothesis generation.

Adaptive Deep Learning for Multiclass Breast Cancer Classification via Misprediction Risk Analysis

Breast cancer remains one of the leading causes of cancer-related deaths worldwide. Early detection is crucial for improving patient outcomes, yet the diagnostic process is often complex and prone to inconsistencies among pathologists. Computer-aided diagnostic approaches have significantly enhanced breast cancer detection, particularly in binary classification (benign vs. malignant). However, these methods face challenges in multiclass classification, leading to frequent mispredictions. In this work, we propose a novel adaptive learning approach for multiclass breast cancer classification using H&E-stained histopathology images. First, we introduce a misprediction risk analysis framework that quantifies and ranks the likelihood of an image being mislabeled by a classifier. This framework leverages an interpretable risk model that requires only a small number of labeled samples for training. Next, we present an adaptive learning strategy that fine-tunes classifiers based on the specific characteristics of a given dataset. This approach minimizes misprediction risk, allowing the classifier to adapt effectively to the target workload. We evaluate our proposed solutions on real benchmark datasets, demonstrating that our risk analysis framework more accurately identifies mispredictions compared to existing methods. Furthermore, our adaptive learning approach significantly improves the performance of state-of-the-art deep neural network classifiers.

Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh

Brain tumors, regardless of being benign or malignant, pose considerable health risks, with malignant tumors being more perilous due to their swift and uncontrolled proliferation, resulting in malignancy. Timely identification is crucial for enhancing patient outcomes, particularly in nations such as Bangladesh, where healthcare infrastructure is constrained. Manual MRI analysis is arduous and susceptible to inaccuracies, rendering it inefficient for prompt diagnosis. This research sought to tackle these problems by creating an automated brain tumor classification system utilizing MRI data obtained from many hospitals in Bangladesh. Advanced deep learning models, including VGG16, VGG19, and ResNet50, were utilized to classify glioma, meningioma, and various brain cancers. Explainable AI (XAI) methodologies, such as Grad-CAM and Grad-CAM++, were employed to improve model interpretability by emphasizing the critical areas in MRI scans that influenced the categorization. VGG16 achieved the most accuracy, attaining 99.17%. The integration of XAI enhanced the system's transparency and stability, rendering it more appropriate for clinical application in resource-limited environments such as Bangladesh. This study highlights the capability of deep learning models, in conjunction with explainable artificial intelligence (XAI), to enhance brain tumor detection and identification in areas with restricted access to advanced medical technologies.

CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning

The integration of Internet of Things (IoT) technology in pulmonary nodule detection significantly enhances the intelligence and real-time capabilities of the detection system. Currently, lung nodule detection primarily focuses on the identification of solid nodules, but different types of lung nodules correspond to various forms of lung cancer. Multi-type detection contributes to improving the overall lung cancer detection rate and enhancing the cure rate. To achieve high sensitivity in nodule detection, targeted improvements were made to the YOLOv8 model. Firstly, the C2f\_RepViTCAMF module was introduced to augment the C2f module in the backbone, thereby enhancing detection accuracy for small lung nodules and achieving a lightweight model design. Secondly, the MSCAF module was incorporated to reconstruct the feature fusion section of the model, improving detection accuracy for lung nodules of varying scales. Furthermore, the KAN network was integrated into the model. By leveraging the KAN network's powerful nonlinear feature learning capability, detection accuracy for small lung nodules was further improved, and the model's generalization ability was enhanced. Tests conducted on the LUNA16 dataset demonstrate that the improved model outperforms the original model as well as other mainstream models such as YOLOv9 and RT-DETR across various evaluation metrics.

MERA: Multimodal and Multiscale Self-Explanatory Model with Considerably Reduced Annotation for Lung Nodule Diagnosis

Lung cancer, a leading cause of cancer-related deaths globally, emphasises the importance of early detection for better patient outcomes. Pulmonary nodules, often early indicators of lung cancer, necessitate accurate, timely diagnosis. Despite Explainable Artificial Intelligence (XAI) advances, many existing systems struggle providing clear, comprehensive explanations, especially with limited labelled data. This study introduces MERA, a Multimodal and Multiscale self-Explanatory model designed for lung nodule diagnosis with considerably Reduced Annotation requirements. MERA integrates unsupervised and weakly supervised learning strategies (self-supervised learning techniques and Vision Transformer architecture for unsupervised feature extraction) and a hierarchical prediction mechanism leveraging sparse annotations via semi-supervised active learning in the learned latent space. MERA explains its decisions on multiple levels: model-level global explanations via semantic latent space clustering, instance-level case-based explanations showing similar instances, local visual explanations via attention maps, and concept explanations using critical nodule attributes. Evaluations on the public LIDC dataset show MERA's superior diagnostic accuracy and self-explainability. With only 1% annotated samples, MERA achieves diagnostic accuracy comparable to or exceeding state-of-the-art methods requiring full annotation. The model's inherent design delivers comprehensive, robust, multilevel explanations aligned closely with clinical practice, enhancing trustworthiness and transparency. Demonstrated viability of unsupervised and weakly supervised learning lowers the barrier to deploying diagnostic AI in broader medical domains. Our complete code is open-source available: https://github.com/diku-dk/credanno.