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Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis.
Publicly available clinical BERT embeddings
Alsentzer, E.; Murphy, J. R.; Boag, W.; Weng, W.-H.; Jin, D.; Naumann, T.; and McDermott, M. 2019 · 1904
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The dangers of post-hoc interpretability: Unjustified counterfactual explanations
Laugel, T.; Lesot, M.-J.; Marsala, C.; Renard, X.; and Detyniecki, M. 2019 · 1907
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The ABCD rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions
Nachbar, F.; Stolz, W.; Merkle, T.; Cognetta, A. B.; Vogt, T.; Landthaler, M.; Bilek, P.; Braun-Falco, O.; and Plewig, G. 1994 · 1994
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Support-vector networks
Cortes, C.; and Vapnik, V. 1995 · 1995
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Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis
Argenziano, G.; Fabbrocini, G.; Carli, P.; De Giorgi, V.; Sammarco, E.; and Delfino, M. 1998 · 1998
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PH 2-A dermoscopic image database for research and benchmarking
Mendonça, T.; Ferreira, P. M.; Marques, J. S.; Marcal, A. R.; and Rozeira, J. 2013 · 2013
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Understanding neural networks through deep visualization
Yosinski, J.; Clune, J.; Nguyen, A.; Fuchs, T.; and Lipson, H. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Nguyen, A.; Dosovitskiy, A.; Yosinski, J.; Brox, T.; and Clune, J. 2016 · 2016
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” Why should i trust you?” Explaining the predictions of any classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
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Pixel recurrent neural networks
Van Den Oord, A.; Kalchbrenner, N.; and Kavukcuoglu, K. 2016 · 2016
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Learning deep features for discriminative localization
Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; and Torralba, A. 2016 · 2016
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Structural compression of convolutional neural networks
Abbasi-Asl, R.; and Yu, B. 2017 · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A.; Kuprel, B.; Novoa, R. A.; Ko, J.; Swetter, S. M.; Blau, H. M.; and Thrun, S. 2017 · 2017
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Understanding black-box predictions via influence functions
Koh, P. W.; and Liang, P. 2017 · 2017
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The doctor just won’t accept that!
Lipton, Z. C. 2017 · 2017
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A survey on deep learning in medical image analysis
Litjens, G.; Kooi, T.; Bejnordi, B. E.; Setio, A. A. A.; Ciompi, F.; Ghafoorian, M.; Van Der Laak, J. A.; Van Ginneken, B.; and Sánchez, C. I. 2017 · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M.; and Lee, S.-I. 2017 · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; and Batra, D. 2017 · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M.; Taly, A.; and Yan, Q. 2017 · 2017
Cited alongside, same era.
Detecting statistical interactions from neural network weights
Tsang, M.; Cheng, D.; and Liu, Y. 2017 · 2017
Cited alongside, same era.
Towards robust interpretability with self-explaining neural networks
Alvarez Melis, D.; and Jaakkola, T. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Concept-based explanation for fine-grained images and its application in infectious keratitis classification
Fang, Z.; Kuang, K.; Lin, Y.; Wu, F.; and Yao, Y.-F. 2020 · 2020
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CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation
Gu, R.; Wang, G.; Song, T.; Huang, R.; Aertsen, M.; Deprest, J.; Ourselin, S.; Vercauteren, T.; and Zhang, S. 2020 · 2020
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Concept bottleneck models
Koh, P. W.; Nguyen, T.; Tang, Y. S.; Mussmann, S.; Pierson, E.; Kim, B.; and Liang, P. 2020 · 2020
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On interpretability of deep learning based skin lesion classifiers using concept activation vectors
Lucieri, A.; Bajwa, M. N.; Braun, S. A.; Malik, M. I.; Dengel, A.; and Ahmed, S. 2020 · 2020
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On completeness-aware concept-based explanations in deep neural networks
Yeh, C.-K.; Kim, B.; Arik, S.; Li, C.-L.; Pfister, T.; and Ravikumar, P. 2020 · 2020
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Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Cited alongside, same era.
A survey of methods for explaining black box models
Guidotti, R.; Monreale, A.; Ruggieri, S.; Turini, F.; Giannotti, F.; and Pedreschi, D. 2018 · 2018
Cited alongside, same era.
Seven-point checklist and skin lesion classification using multitask multimodal neural nets
Kawahara, J.; Daneshvar, S.; Argenziano, G.; and Hamarneh, G. 2018 · 2018
Cited alongside, same era.
Identifying medical diagnoses and treatable diseases by image-based deep learning
Kermany, D. S.; Goldbaum, M.; Cai, W.; Valentim, C. C.; Liang, H.; Baxter, S. L.; McKeown, A.; Yang, G.; Wu, X.; Yan, F.; et al. 2018 · 2018
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Kim, B.; Wattenberg, M.; Gilmer, J.; Cai, C.; Wexler, J.; Viegas, F.; et al. 2018 · 2018
Cited alongside, same era.
Machine learning interpretability: A survey on methods and metrics
Carvalho, D. V.; Pereira, E. M.; and Cardoso, J. S. 2019 · 2019
Cited alongside, same era.
Faithful and customizable explanations of black box models
Lakkaraju, H.; Kamar, E.; Caruana, R.; and Leskovec, J. 2019 · 2019
Cited alongside, same era.
Explainable skin lesion diagnosis using taxonomies
Barata, C.; Celebi, M. E.; and Marques, J. S. 2021 · 2021
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Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset
Groh, M.; Harris, C.; Soenksen, L.; Lau, F.; Han, R.; Kim, A.; Koochek, A.; and Badri, O. 2021 · 2021
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Roadmap of designing cognitive metrics for explainable artificial intelligence (XAI)
Hsiao, J. H.-w.; Ngai, H. H. T.; Qiu, L.; Yang, Y.; and Cao, C. C. 2021 · 2021
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Explaining in style: Training a gan to explain a classifier in stylespace
Lang, O.; Gandelsman, Y.; Yarom, M.; Wald, Y.; Elidan, G.; Hassidim, A.; Freeman, W. T.; Isola, P.; Globerson, A.; Irani, M.; et al. 2021 · 2021
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Attention-based interpretability with concept transformers
Rigotti, M.; Miksovic, C.; Giurgiu, I.; Gschwind, T.; and Scotton, P. 2021 · 2021
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Skincon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
Daneshjou, R.; Yuksekgonul, M.; Cai, Z. R.; Novoa, R.; and Zou, J. Y. 2022 · 2022
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A Framework for Learning Ante-Hoc Explainable Models via Concepts
Sarkar, A.; Vijaykeerthy, D.; Sarkar, A.; and Balasubramanian, V. N. 2022 · 2022
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Contrastive learning of medical visual representations from paired images and text
Zhang, Y.; Jiang, H.; Miura, Y.; Manning, C. D.; and Langlotz, C. P. 2022 · 2022
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Guidelines and evaluation of clinical explainable AI in medical image analysis
Jin, W.; Li, X.; Fatehi, M.; and Hamarneh, G. 2023 · 2023
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Coherent Concept-Based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis
Patrício, C.; Neves, J. a. C.; Teixeira, L. F.; et al. 2023 · 2023
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Towards Trustable Skin Cancer Diagnosis via Rewriting Model’s Decision
Yan, S.; Yu, Z.; Zhang, X.; Mahapatra, D.; Chandra, S. S.; Janda, M.; Soyer, P.; and Ge, Z. 2023 · 2023
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Post-hoc Concept Bottleneck Models
Yuksekgonul, M.; Wang, M.; Zou, J.; et al. 2023 · 2023
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