Pieter Van Molle

PhD Thesis

Integrating neural networks as diagnosis support in dermatology

Pieter Van Molle (UGent)

(2024)

Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis : complementary information that may affect decision-making

Pieter Van Molle (UGent) , Sofie Mylle (UGent) , Tim Verbelen (UGent) , Cedric De Boom, Bert Vankeirsbilck (UGent) , Evelien Verhaeghe (UGent) , Bart Dhoedt (UGent) and Lieve Brochez (UGent)

(2023) EXPERIMENTAL DERMATOLOGY. 32(10). p.1744-1751

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The value of measuring uncertainty in neural networks in dermoscopy

Pieter Van Molle (UGent) , Lieve Brochez (UGent) , Tim Verbelen (UGent) , Cedric De Boom, Bert Vankeirsbilck (UGent) , Evelien Verhaeghe (UGent) , Sofie Mylle (UGent) , Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2022) JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY. 87(5). p.1191-1193

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Journal Article
A1
open access

Data-efficient sensor upgrade path using knowledge distillation

Pieter Van Molle (UGent) , Cedric De Boom, Tim Verbelen (UGent) , Bert Vankeirsbilck (UGent) , Jonas De Vylder (UGent) , Bart Diricx, Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2021) SENSORS. 21(19).

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open access

Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

Pieter Van Molle (UGent) , Tim Verbelen (UGent) , Bert Vankeirsbilck (UGent) , Jonas De Vylder (UGent) , Bart Diricx, Tom Kimpe, Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2021) NEURAL COMPUTING & APPLICATIONS. 33(16). p.10259-10275

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open access

Learning robots to grasp by demonstration

Elias De Coninck, Tim Verbelen (UGent) , Pieter Van Molle (UGent) , Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2020) ROBOTICS AND AUTONOMOUS SYSTEMS. 127.

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Learning to grasp arbitrary household objects from a single demonstration

Elias De Coninck, Tim Verbelen (UGent) , Pieter Van Molle (UGent) , Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2019) 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). In IEEE International Conference on Intelligent Robots and Systems p.2372-2377

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Quantifying uncertainty of deep neural networks in skin lesion classification

Pieter Van Molle (UGent) , Tim Verbelen (UGent) , Cedric De Boom, Bert Vankeirsbilck (UGent) , Jonas De Vylder (UGent) , Bart Diricx, Tom Kimpe (UGent) , Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2019) UNCERTAINTY FOR SAFE UTILIZATION OF MACHINE LEARNING IN MEDICAL IMAGING AND CLINICAL IMAGE-BASED PROCEDURES. In Lecture Notes in Computer Science 11840. p.52-61

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Assessing uncertainty for computer assisted skin lesion diagnosis

Pieter Van Molle (UGent) , Tim Verbelen (UGent) , Bert Vankeirsbilck (UGent) , Sofie Mylle, Evelien Verhaeghe (UGent) , Lieve Brochez (UGent) , Jonas De Vylder (UGent) , Bart Diricx, Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2019) EADV, 28th Congress, Abstracts.

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Journal Article
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open access

Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things

Sam Leroux (UGent) , Steven Bohez (UGent) , Elias De Coninck, Pieter Van Molle (UGent) , Bert Vankeirsbilck (UGent) , Tim Verbelen (UGent) , Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2019) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 97. p.355-360

Academic Bibliography

Pieter Van Molle

Integrating neural networks as diagnosis support in dermatology

Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis : complementary information that may affect decision-making

The value of measuring uncertainty in neural networks in dermoscopy

Data-efficient sensor upgrade path using knowledge distillation

Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

Learning robots to grasp by demonstration

Learning to grasp arbitrary household objects from a single demonstration

Quantifying uncertainty of deep neural networks in skin lesion classification

Assessing uncertainty for computer assisted skin lesion diagnosis

Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things

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