Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification

By Pieter Van Molle, Miguel De Strooper, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt. 2018

59 citations recorded by the source corpus.

Because of their state-of-the-art performance in computer vision, CNNs are becoming increasingly popular in a variety of fields, including medicine. However, as neural networks are black box function approximators, it is difficult, if not impossible, for a medical expert to reason about their output. This could potentially result in the expert distrusting the network when he or she does not agree with its output. In such a case, explaining why the CNN makes a certain decision becomes valuable information. In this paper, we try to open the black box of the CNN by inspecting and visualizing the learned feature maps, in the field of dermatology. We show that, to some extent, CNNs focus on features similar to those used by dermatologists to make a diagnosis. However, more research is required for fully explaining their output.

Subfields: Artificial Intelligence, Oncology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Amitojdeep Singh
2. Cuntai Guan
3. Vasudevan Lakshminarayanan
4. Erico Tjoa
5. Sourya Sengupta

Citations in the corpus

• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “brain, neuro* breast [68], lung [6], [81], [47], [67], [130], [152] sleep [153], skin [154] [131], [132], [135], [155] others [105] Method.”
• Explainable Deep Learning Models in Medical Image Analysis (2020, background): “Another study [44] visualized the CNN features for skin lesion classification.”