Diagnostic Radiology

Med-CAM: Minimal Evidence for Explaining Medical Decision Making

Reliable and interpretable decision-making is essential in medical imaging, where diagnostic outcomes directly influence patient care. Despite advances in deep learning, most medical AI systems operate as opaque black boxes, providing little insight into why a particular diagnosis was reached. In this paper, we introduce Med-CAM, a framework for generating minimal and sharp maps as evidence-based ...

MApLe: Multi-instance Alignment of Diagnostic Reports and Large Medical Images

In diagnostic reports, experts encode complex imaging data into clinically actionable information. They describe subtle pathological findings that are meaningful in their anatomical context. Reports follow relatively consistent structures, expressing diagnostic information with few words that are often associated with tiny but consequential image observations. Standard vision language models strug...

Virtual Spectral Decomposition with Dendritic Tile Selection: An Explainable AI Framework for Multimodal Tissue Composition Analysis and Immune Phenotyping Across Pancreatic, Lung, and Breast Cancer

Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explain...

From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction

Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. Howev...

Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification

The rise of multimodal large language models (MLLMs) has sparked an unprecedented wave of applications in the field of medical imaging analysis. Howev...

A Self supervised learning framework for imbalanced medical imaging datasets

Two problems often plague medical imaging analysis: 1) Non-availability of large quantities of labeled training data, and 2) Dealing with imbalanced d...

Artificial Intelligence and Circulating microRNA Signatures for Early Breast Cancer Detection: A Systematic Review and Meta-Analysis

Background: Early breast cancer detection remains central to improving clinical outcomes, yet conventional screening pathways, particularly mammograph...

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high...

FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants

While powerful in image-conditioned generation, multimodal large language models (MLLMs) can display uneven performance across demographic groups, hig...

Bi-CRCL: Bidirectional Conservative-Radical Complementary Learning with Pre-trained Foundation Models for Class-incremental Medical Image Analysis

Class-incremental learning (CIL) in medical image-guided diagnosis requires retaining prior diagnostic knowledge while adapting to newly emerging dise...

AI-Assisted Pneumonia Detection, Localisation and Report Generation from Chest X-rays

Purpose: Pneumonia detection in chest X-rays (CXRs) is complicated by high inter-observer variability and overlapping radiographic patterns. While dee...

HybridNet-XR: Efficient Teacher-Free Self-Supervised Learning for Autonomous Medical Diagnostic Systems in Resource-Constrained Environments.

Deep learning model classification on large datasets is often limited in countries with restricted computational resources. While transfer learning ca...

Addressing Data Scarcity in 3D Trauma Detection through Self-Supervised and Semi-Supervised Learning with Vertex Relative Position Encoding

Accurate detection and localization of traumatic injuries in abdominal CT scans remains a critical challenge in emergency radiology, primarily due to ...

Adversarial Robustness of Capsule Networks for Medical Image Classification

Purpose: Deep learning models are increasingly being used in medical diagnostics, but their vulnerability to adversarial perturbations raises concerns...

Interactive Medical-SAM2 GUI: A Napari-based semi-automatic annotation tool for medical images

Interactive Medical-SAM2 GUI is an open-source desktop application for semi-automatic annotation of 2D and 3D medical images. Built on the Napari mult...

SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation

Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging moda...

3D, multi-omic imaging reveals molecular biomarkers of the pre-metastatic niche in lung cancer

The recurrence rate following complete surgical resection of primary non-small cell lung cancer is as high as 55%, yet no approach currently exists to...

Improving Medical Visual Reinforcement Fine-Tuning via Perception and Reasoning Augmentation

While recent advances in Reinforcement Fine-Tuning (RFT) have shown that rule-based reward schemes can enable effective post-training for large langua...

WristMIR: Coarse-to-Fine Region-Aware Retrieval of Pediatric Wrist Radiographs with Radiology Report-Driven Learning

Retrieving wrist radiographs with analogous fracture patterns is challenging because clinically important cues are subtle, highly localized and often ...

Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity

3D medical imaging is in high demand and essential for clinical diagnosis and scientific research. Currently, diffusion models (DMs) have become an ef...