Oncology/Hematology

Diffusion models applied to skin and oral cancer classification

This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifical...

Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning

Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Immunohistochemistry (IHC) biomarkers like HER2, ER, and PR are critical for identifying breast cancer subtypes. However, traditional IHC classification relies on pathologists' expertise, making it labor-intensive and subject to significant inter-observer variability. To ...

A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)

Background: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recogni...

AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. A crucial challenge for early diagnosis is differentiating unce...

Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation

Accurate segmentation of glioma brain tumors is crucial for diagnosis and treatment planning. Deep learning techniques offer promising solutions, bu...

ProtoBERT-LoRA: Parameter-Efficient Prototypical Finetuning for Immunotherapy Study Identification

Identifying immune checkpoint inhibitor (ICI) studies in genomic repositories like Gene Expression Omnibus (GEO) is vital for cancer research yet re...

Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining

The differentiation between pathological subtypes of non-small cell lung cancer (NSCLC) is an essential step in guiding treatment options and progno...

Single Shot AI-assisted quantification of KI-67 proliferation index in breast cancer

Reliable quantification of Ki-67, a key proliferation marker in breast cancer, is essential for molecular subtyping and informed treatment planning....

Improved tissue sodium concentration quantification in breast cancer by reducing partial volume effects: a preliminary study

Introduction: In sodium (23Na) MRI, partial volume effects (PVE) are one of the most common causes of errors in the quantification of tissue sodium ...

ELM: Ensemble of Language Models for Predicting Tumor Group from Pathology Reports

Population-based cancer registries (PBCRs) face a significant bottleneck in manually extracting data from unstructured pathology reports, a process ...

PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation

Medical image segmentation, particularly for brain tumor analysis, demands precise and computationally efficient models due to the complexity of mul...

TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy

Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radio...

Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology.

This study aims to create a deep learning-based classification model for cervical cancer biopsy before and during radiotherapy, visualize the results ...

Histomorphology-driven multi-instance learning for breast cancer WSI classification

Histomorphology is crucial in breast cancer diagnosis. However, existing whole slide image (WSI) classification methods struggle to effectively inco...

PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images

Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different reg...

Achieving Dependability of AI Execution with Radiation Hardened Processors

The reliance on radiation-hardened hardware, essential for domains requiring high-dependability such as space, nuclear energy and medical applicatio...

ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology

Prediction tasks in digital pathology are challenging due to the massive size of whole-slide images (WSIs) and the weak nature of training signals. ...

Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images

Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anato...

Ex vivo experiment on vertebral body with defect representing bone metastasis

Osteolytic metastases located in the vertebrae reduce strength and enhance the risk of vertebral fractures. This risk can be predicted by means of v...

Interpretable Machine Learning for Oral Lesion Diagnosis through Prototypical Instances Identification

Decision-making processes in healthcare can be highly complex and challenging. Machine Learning tools offer significant potential to assist in these...