A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Speech

Cases of laryngeal cancer are predicted to rise significantly in the coming years. Current diagnostic pathways cause many patients to be incorrectly referred to urgent suspected cancer pathways, putting undue stress on both patients and the medical system. Artificial intelligence offers a promising solution by enabling non-invasive detection of laryngeal cancer from patient speech, which could help prioritise referrals more effectively and reduce inappropriate referrals of non-cancer patients. To realise this potential, open science is crucial. A major barrier in this field is the lack of open-source datasets and reproducible benchmarks, forcing researchers to start from scratch. Our work addresses this challenge by introducing a benchmark suite comprising 36 models trained and evaluated on open-source datasets. These models are accessible in a public repository, providing a foundation for future research. They evaluate three different algorithms and three audio feature sets, offering a comprehensive benchmarking framework. We propose standardised metrics and evaluation methodologies to ensure consistent and comparable results across future studies. The presented models include both audio-only inputs and multimodal inputs that incorporate demographic and symptom data, enabling their application to datasets with diverse patient information. By providing these benchmarks, future researchers can evaluate their datasets, refine the models, and use them as a foundation for more advanced approaches. This work aims to provide a baseline for establishing reproducible benchmarks, enabling researchers to compare new methods against these standards and ultimately advancing the development of AI tools for detecting laryngeal cancer.

Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images

Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by combining MRI's detailed visualization with TRUS's real-time guidance, it is a complex and time-intensive procedure that relies heavily on manual annotations, leading to potential errors. To address these challenges, we propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations. Unlike traditional multimodal fusion approaches that rely on naive data concatenation, our method integrates a registration-segmentation framework to align and leverage spatial information between MRI and TRUS modalities. This alignment enhances segmentation accuracy and reduces reliance on manual effort. Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132) methods, with significant improvements (p $<$ 0.01). This framework demonstrates the potential for reducing the complexity of prostate cancer diagnosis and provides a flexible architecture applicable to other multimodal medical imaging tasks.

Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models

This research presents an innovative approach to cancer diagnosis and prediction using explainable Artificial Intelligence (XAI) and deep learning techniques. With cancer causing nearly 10 million deaths globally in 2020, early and accurate diagnosis is crucial. Traditional methods often face challenges in cost, accuracy, and efficiency. Our study develops an AI model that provides precise outcomes and clear insights into its decision-making process, addressing the "black box" problem of deep learning models. By employing XAI techniques, we enhance interpretability and transparency, building trust among healthcare professionals and patients. Our approach leverages neural networks to analyse extensive datasets, identifying patterns for cancer detection. This model has the potential to revolutionise diagnosis by improving accuracy, accessibility, and clarity in medical decision-making, possibly leading to earlier detection and more personalised treatment strategies. Furthermore, it could democratise access to high-quality diagnostics, particularly in resource-limited settings, contributing to global health equity. The model's applications extend beyond cancer diagnosis, potentially transforming various aspects of medical decision-making and saving millions of lives worldwide.

SCFANet: Style Distribution Constraint Feature Alignment Network For Pathological Staining Translation

Immunohistochemical (IHC) staining serves as a valuable technique for detecting specific antigens or proteins through antibody-mediated visualization. However, the IHC staining process is both time-consuming and costly. To address these limitations, the application of deep learning models for direct translation of cost-effective Hematoxylin and Eosin (H&E) stained images into IHC stained images has emerged as an efficient solution. Nevertheless, the conversion from H&E to IHC images presents significant challenges, primarily due to alignment discrepancies between image pairs and the inherent diversity in IHC staining style patterns. To overcome these challenges, we propose the Style Distribution Constraint Feature Alignment Network (SCFANet), which incorporates two innovative modules: the Style Distribution Constrainer (SDC) and Feature Alignment Learning (FAL). The SDC ensures consistency between the generated and target images' style distributions while integrating cycle consistency loss to maintain structural consistency. To mitigate the complexity of direct image-to-image translation, the FAL module decomposes the end-to-end translation task into two subtasks: image reconstruction and feature alignment. Furthermore, we ensure pathological consistency between generated and target images by maintaining pathological pattern consistency and Optical Density (OD) uniformity. Extensive experiments conducted on the Breast Cancer Immunohistochemical (BCI) dataset demonstrate that our SCFANet model outperforms existing methods, achieving precise transformation of H&E-stained images into their IHC-stained counterparts. The proposed approach not only addresses the technical challenges in H&E to IHC image translation but also provides a robust framework for accurate and efficient stain conversion in pathological analysis.

How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark

Recently, histopathology vision-language foundation models (VLMs) have gained popularity due to their enhanced performance and generalizability across different downstream tasks. However, most existing histopathology benchmarks are either unimodal or limited in terms of diversity of clinical tasks, organs, and acquisition instruments, as well as their partial availability to the public due to patient data privacy. As a consequence, there is a lack of comprehensive evaluation of existing histopathology VLMs on a unified benchmark setting that better reflects a wide range of clinical scenarios. To address this gap, we introduce HistoVL, a fully open-source comprehensive benchmark comprising images acquired using up to 11 various acquisition tools that are paired with specifically crafted captions by incorporating class names and diverse pathology descriptions. Our Histo-VL includes 26 organs, 31 cancer types, and a wide variety of tissue obtained from 14 heterogeneous patient cohorts, totaling more than 5 million patches obtained from over 41K WSIs viewed under various magnification levels. We systematically evaluate existing histopathology VLMs on Histo-VL to simulate diverse tasks performed by experts in real-world clinical scenarios. Our analysis reveals interesting findings, including large sensitivity of most existing histopathology VLMs to textual changes with a drop in balanced accuracy of up to 25% in tasks such as Metastasis detection, low robustness to adversarial attacks, as well as improper calibration of models evident through high ECE values and low model prediction confidence, all of which can affect their clinical implementation.

A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia

Oral cancer constitutes a significant global health concern, resulting in 277,484 fatalities in 2023, with the highest prevalence observed in low- and middle-income nations. Facilitating automation in the detection of possibly malignant and malignant lesions in the oral cavity could result in cost-effective and early disease diagnosis. Establishing an extensive repository of meticulously annotated oral lesions is essential. In this research photos are being collected from global clinical experts, who have been equipped with an annotation tool to generate comprehensive labelling. This research presents a novel approach for integrating bounding box annotations from various doctors. Additionally, Deep Belief Network combined with CAPSNET is employed to develop automated systems that extracted intricate patterns to address this challenging problem. This study evaluated two deep learning-based computer vision methodologies for the automated detection and classification of oral lesions to facilitate the early detection of oral cancer: image classification utilizing CAPSNET. Image classification attained an F1 score of 94.23% for detecting photos with lesions 93.46% for identifying images necessitating referral. Object detection attained an F1 score of 89.34% for identifying lesions for referral. Subsequent performances are documented about classification based on the sort of referral decision. Our preliminary findings indicate that deep learning possesses the capability to address this complex problem.

Conformal Risk Control for Pulmonary Nodule Detection

Quantitative tools are increasingly appealing for decision support in healthcare, driven by the growing capabilities of advanced AI systems. However, understanding the predictive uncertainties surrounding a tool's output is crucial for decision-makers to ensure reliable and transparent decisions. In this paper, we present a case study on pulmonary nodule detection for lung cancer screening, enhancing an advanced detection model with an uncertainty quantification technique called conformal risk control (CRC). We demonstrate that prediction sets with conformal guarantees are attractive measures of predictive uncertainty in the safety-critical healthcare domain, allowing end-users to achieve arbitrary validity by trading off false positives and providing formal statistical guarantees on model performance. Among ground-truth nodules annotated by at least three radiologists, our model achieves a sensitivity that is competitive with that generally achieved by individual radiologists, with a slight increase in false positives. Furthermore, we illustrate the risks of using off-the-shelve prediction models when faced with ontological uncertainty, such as when radiologists disagree on what constitutes the ground truth on pulmonary nodules.

Brain Tumor Identification using Improved YOLOv8

Identifying the extent of brain tumors is a significant challenge in brain cancer treatment. The main difficulty is in the approximate detection of tumor size. Magnetic resonance imaging (MRI) has become a critical diagnostic tool. However, manually detecting the boundaries of brain tumors from MRI scans is a labor-intensive task that requires extensive expertise. Deep learning and computer-aided detection techniques have led to notable advances in machine learning for this purpose. In this paper, we propose a modified You Only Look Once (YOLOv8) model to accurately detect the tumors within the MRI images. The proposed model replaced the Non-Maximum Suppression (NMS) algorithm with a Real-Time Detection Transformer (RT- DETR) in the detection head. NMS filters out redundant or overlapping bounding boxes in the detected tumors, but they are hand-designed and pre-set. RT-DETR removes hand-designed components. The second improvement was made by replacing the normal convolution block with ghost convolution. Ghost Convolution reduces computational and memory costs while maintaining high accuracy and enabling faster inference, making it ideal for resource-constrained environments and real-time applications. The third improvement was made by introducing a vision transformer block in the backbone of YOLOv8 to extract context-aware features. We used a publicly available dataset of brain tumors in the proposed model. The proposed model performed better than the original YOLOv8 model and also performed better than other object detectors (Faster R- CNN, Mask R-CNN, YOLO, YOLOv3, YOLOv4, YOLOv5, SSD, RetinaNet, EfficientDet, and DETR). The proposed model achieved 0.91 mAP (mean Average Precision)@0.5.

Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.

MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and Detection

Problem: Pancreas radiological imaging is challenging due to the small size, blurred boundaries, and variability of shape and position of the organ among patients. Goal: In this work we present MiniGPT-Pancreas, a Multimodal Large Language Model (MLLM), as an interactive chatbot to support clinicians in pancreas cancer diagnosis by integrating visual and textual information. Methods: MiniGPT-v2, a general-purpose MLLM, was fine-tuned in a cascaded way for pancreas detection, tumor classification, and tumor detection with multimodal prompts combining questions and computed tomography scans from the National Institute of Health (NIH), and Medical Segmentation Decathlon (MSD) datasets. The AbdomenCT-1k dataset was used to detect the liver, spleen, kidney, and pancreas. Results: MiniGPT-Pancreas achieved an Intersection over Union (IoU) of 0.595 and 0.550 for the detection of pancreas on NIH and MSD datasets, respectively. For the pancreas cancer classification task on the MSD dataset, accuracy, precision, and recall were 0.876, 0.874, and 0.878, respectively. When evaluating MiniGPT-Pancreas on the AbdomenCT-1k dataset for multi-organ detection, the IoU was 0.8399 for the liver, 0.722 for the kidney, 0.705 for the spleen, and 0.497 for the pancreas. For the pancreas tumor detection task, the IoU score was 0.168 on the MSD dataset. Conclusions: MiniGPT-Pancreas represents a promising solution to support clinicians in the classification of pancreas images with pancreas tumors. Future research is needed to improve the score on the detection task, especially for pancreas tumors.