Super-trustscore: reliable failure detection for automated skin lesion diagnosis

Super-trustscore: reliable failure detection for automated skin lesion diagnosis

The successful deployment of deep neural networks in safetycritical settings, such as medical image analysis, is contingent on their ability to provide reliable uncertainty estimates. In this paper, we propose a new confidence scoring function called Super-TrustScore that improves upon the existing...

Descrizione completa

Dettagli Bibliografici
Autori principali:	Naushad, J, Voiculescu, ID
Natura:	Conference item
Lingua:	English
Pubblicazione:	IEEE 2024

Documenti analoghi

AI EMPOWERED DIAGNOSIS OF PEMPHIGUS: A MACHINE LEARNING APPROACH FOR AUTOMATED SKIN LESION DETECTION
di: Mamun Ahmed, et al.
Pubblicazione: (2023-12-01)

Exploring Edge-Based Segmentation Towards Automated Skin Lesion Diagnosis
di: Lau, Hui Keng, et al.
Pubblicazione: (2018)

Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis
di: Paul Wighton, et al.
Pubblicazione: (2011-01-01)

Automated stroke lesion detection and diagnosis system
di: Mohd Saad, N., et al.
Pubblicazione: (2017)

A Reinforcement Learning Algorithm for Automated Detection of Skin Lesions
di: Usman Ahmad Usmani, et al.
Pubblicazione: (2021-10-01)

Automated skin lesion detection and prevalence estimation in Tamanend's bottlenose dolphins
di: Colin J. Murphy, et al.
Pubblicazione: (2025-03-01)

Fluorescence images of skin lesions and automated diagnosis using convolutional neural networks
di: Matheus B. Rocha, et al.
Pubblicazione: (2025-04-01)

LesionAir : a low-cost tool for automated skin cancer diagnosis and mapping
di: Wortman, Tyler David
Pubblicazione: (2016)

Automated Skin Lesion Classification on Ultrasound Images
di: Péter Marosán-Vilimszky , et al.
Pubblicazione: (2021-07-01)

An agent-based inference engine for efficient and reliable automated car failure diagnosis assistance
di: A. Mostafa, Salama, et al.
Pubblicazione: (2018)

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification
di: M Mohamed Musthafa, et al.
Pubblicazione: (2024-08-01)

DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS
di: D. A. Gavrilov, et al.
Pubblicazione: (2019-05-01)

Mobile Health for Diagnosis and Management of Skin Lesions
di: Hamed Moghaddasi, et al.
Pubblicazione: (2016-09-01)

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques
di: Nourelhoda M. Mahmoud, et al.
Pubblicazione: (2024-04-01)

MONTENEGRO SKIN TEST AND AGE OF SKIN LESION AS PREDICTORS OF TREATMENT FAILURE IN CUTANEOUS LEISHMANIASIS
di: Liliane de Fátima Antonio, et al.
Pubblicazione: (2014-09-01)

Automatic Detection and Categorization of Skin Lesions for Early Diagnosis of Skin Cancer Using YOLO-v3 - DCNN Architecture
di: S. Oswalt Manoj, et al.
Pubblicazione: (2023-07-01)

SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis
di: Geetika Munjal, et al.
Pubblicazione: (2024-12-01)

Automated detection and monitoring of methane super-emitters using satellite data
di: B. J. Schuit, et al.
Pubblicazione: (2023-09-01)

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification
di: Madhumita Pal, et al.
Pubblicazione: (2023-08-01)

DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion Segmentation
di: Ali Karimi, et al.
Pubblicazione: (2023-01-01)

Unbiased approach for virus detection in skin lesions.
di: Davit Bzhalava, et al.
Pubblicazione: (2013-01-01)

Reliability improvement in automated incident detection (AID) /
di: Tohid Akhlaghi Moghadam, 1986-, et al.
Pubblicazione: (2013)

LesionNet: an automated approach for skin lesion classification using SIFT features with customized convolutional neural network
di: Sarah A. Alzakari, et al.
Pubblicazione: (2024-10-01)

Artificial Intelligence-Based Skin Lesion Analysis and Skin Cancer Detection
di: Momina Qureshi, et al.
Pubblicazione: (2025-01-01)

Risk Assessment of Substation Automation System Failure Considering Human Reliability
di: Yu HUANG, et al.
Pubblicazione: (2019-10-01)

Diagnosis of pseudoxanthoma elasticum in a patient with discrete skin lesions
di: Catalina Jahr, et al.
Pubblicazione: (2023-08-01)

Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions
di: Hassan El-Khatib, et al.
Pubblicazione: (2020-03-01)

Spectrophotometric intracutaneous analysis for differential diagnosis of pigmented skin lesions
di: Е. V. Filonenko, et al.
Pubblicazione: (2013-03-01)

Spectrophotometric intracutaneous analysis for differential diagnosis of pigmented skin lesions
di: Е. V. Filonenko, et al.
Pubblicazione: (2013-01-01)

LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool
di: Carlson, Jay D., et al.
Pubblicazione: (2019)

Automated Analysis of Ultrasound Videos for Detection of Breast Lesions
di: Mohammad Mehdi Movahedi, et al.
Pubblicazione: (2020-01-01)

Automated detection of Lupus white matter lesions in MRI
di: Eloy Roura Perez, et al.
Pubblicazione: (2016-08-01)

Multi-organ failure with necrotic skin lesions due to infection with Chlamydia psittaci
di: Renske Meijer, et al.
Pubblicazione: (2021-05-01)

Skin lesion detection and recognition via deep learning
di: Chen, Ziyu
Pubblicazione: (2022)

The Use of Artificial Intelligence to Detect Malignant Skin Lesions
di: Sofia Haddadin, et al.
Pubblicazione: (2024-06-01)

Skin Lesion Detection Algorithms in Whole Body Images
di: Michał H. Strzelecki, et al.
Pubblicazione: (2021-10-01)

Feasible Skin Lesion Detection using CNN and RNN
di: Ram Kumar R.P., et al.
Pubblicazione: (2023-01-01)

Is Magnetic Resonance Imaging (MRI) Reliable in the Diagnosis of Osteochondral Lesions (OCL’s)?
di: Triin Nurm MD, et al.
Pubblicazione: (2017-09-01)

Automated mosaicing with super-resolution zoom
di: Capel, D, et al.
Pubblicazione: (1998)

Hyper redundancy for super reliable FPGAs
di: Sergey Tyurin
Pubblicazione: (2021-02-01)