Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt
Open access

Calibration and Reliability of Deep Learning in Medical Imaging Classification: Impact of Image Resolution.

K. Mridha Madhu Shukla
Sep 2026 · Journal of imaging informatics in medicine · 0 citations · 6 references
Medicine

TL;DR

The findings indicate that variations in image resolution can significantly influence model calibration and suggest that accuracy alone may not fully capture model reliability under varying input conditions and that resolution sensitivity is task dependent.

DOI PubMed Semantic Scholar
Read PDF

Similar papers

Open access Sep 2026

INFORMER-Interpretability-Founded Monitoring of Medical Image Deep Learning Models: Application to Chest X-ray Pathologies.

Findings indicate that gradient-level and graph-based analysis can enhance the effectiveness, transparency, and clinical applicability of quality control in multi-label medical image classification.

Shelley Zixin Shu, Aurélie Pahud de Mortanges, A. Poellinger et al. · 0 citations
Open access Sep 2026

Performance-interpretability trade-offs and generalization in deep learning for pneumonia detection: A benchmarking study

A reproducible, end-to-end methodology that jointly optimizes performance and interpretability, offering practical guidance for selecting and deploying explainable deep learning models in clinical pneumonia screening is contributed.

Francisco A. Gómez-Vela, Aurelio López-Fernández, F. Divina et al. · 0 citations
Review Open access Aug 2026

Reliable and explainable deep learning for brain tumor diagnosis: Uncertainty-calibrated MRI classification with external validation

Reliable brain-tumor diagnosis from magnetic resonance imaging (MRI) requires more than high classification accuracy. Deep neural networks can be overconfident under scanner, protocol, demographic, and disease-spectrum shifts, while visually plausible explanations may not faithfully identify the evidence driving a pred...

Nazmul Hasan, Mohammad Mahmudul Hasan Bhuyain · 0 citations
#artificial intelligence Preprint Oct 2026

Impact of Data Augmentation on Confidence Calibration in Melanoma Classification

Accurately quantifying the predictive uncertainty or improving model calibration plays an important role in medical image classification, in particular in melanoma diagnosis, where accurate uncertainty quantification can have significant implications for patient care. One of the methods for calibration improvement is d...

Morgan May, Simon Caton, Pierpaolo Dondio · 0 citations
Aug 2026

Multi-view explainability and ensemble deep learning for prostate lesion classification: A comprehensive study.

These findings suggest that interpretability quality can be quantitatively assessed and improved through multi-method and ensemble-based analysis and enhances explanation robustness and supports reliable and transparent clinical decision support for prostate magnetic resonance imaging.

M. Gulum, M. Kantardzic, Christopher M. Trombley et al. · 0 citations
Open access Oct 2026

A MULTIMODAL DEEP LEARNING FRAMEWORK FOR IMPROVING THE CLINICAL RELIABILITY OF ULTRASOUND-BASED LIVER STEATOSIS ASSESSMENT

Objectives: This study aims to examine the limitations of existing deep learning approaches in ultrasound-based liver steatosis evaluation and to develop a framework that enhances clinical reliability and real-world applicability. Methodology: A structured pipeline is proposed that integrates diverse data acquisition,...

Sahaya Mercy A. Sjc, Arockia Sahaya Sheela G · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.