Fetching the paper…
Reading the bibliography…
We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications.
Tonekaboni, S., Joshi, S., McCradden, M.D., Goldenberg, A., 2019 · 1905
Earlier work this paper cites.
Joshi, S., Koyejo, O., Vijitbenjaronk, W., Kim, B., Ghosh, J., 2019 · 1907
Earlier work this paper cites.
Interpretable Counterfactual Explanations Guided by Prototypes
Van Looveren, A., Klaise, J., 2019 · 1907
Earlier work this paper cites.
A simple approach to ordinal classification
Frank, E., Hall, M., 2001 · 2001
Earlier work this paper cites.
Automated Cardiothoracic Ratio Calculation and Cardiomegaly Detection using Deep Learning Approach
Chamveha, I., Promwiset, T., Tongdee, T., Saiviroonporn, P., Chaisangmongkon, W., 2020 · 2002
Earlier work this paper cites.
Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database
van Ginneken, B., Stegmann, M.B., Loog, M., 2006 · 2005
Earlier work this paper cites.
Fleischner Society: Glossary of terms for thoracic imaging
Hansell, D.M., Bankier, A.A., MacMahon, H., et al., 2008 · 2008
Earlier work this paper cites.
Cardiothoracic ratio from postero-anterior chest radiographs: A simple, reproducible and independent marker of disease severity and outcome in adults with congenital heart disease
Dimopoulos, K., Giannakoulas, G., Bendayan, I., Liodakis, E., Petraco, R., Diller, G.P., Piepoli, M.F., Swan, L., Mullen, M., Best, N., Poole-Wilson, P.A., Francis, D.P., Rubens, M.B., Gatzoulis, M.A., 2013 · 2013
Earlier work this paper cites.
Automated localization of costophrenic recesses and costophrenic angle measurement on frontal chest radiographs
Maduskar, P., Hogeweg, L., Philipsen, R., van Ginneken, B., 2013 · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., Zisserman, A., 2013 · 2013
Earlier work this paper cites.
Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G., 2014 · 2014
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., Zisserman, A., 2014 · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W., 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D.P., Ba, J., 2015 · 2015
Earlier work this paper cites.
Establishing the Cardiothoracic Ratio Using Chest Radiographs in an Indigenous Ghanaian Population: A Simple Tool for Cardiomegaly Screening
Mensah, Y., Mensah, K., Asiamah, S., Gbadamosi, H., Idun, E., Brakohiapa, W., Oddoye, A., 2015 · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J., 2015 · 2015
Earlier work this paper cites.
U-net convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Earlier work this paper cites.
Striving for Simplicity: The All Convolutional Net
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A., 2015 · 2015
Earlier work this paper cites.
Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Àgata Lapedriza, Oliva, A., Torralba, A., 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q., 2016 · 2016
Earlier work this paper cites.
Automatic detection of pleural effusion in chest radiographs
Maduskar, P., Philipsen, R.H., Melendez, J., Scholten, E., Chanda, D., Ayles, H., Sánchez, C.I., van Ginneken, B., 2016 · 2016
Earlier work this paper cites.
Evaluating the visualization of what a deep neural network has learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., Müller, K.R., 2017 · 2016
Earlier work this paper cites.
labelme Image Polygonal Annotation with Python
Wada, K., 2016 · 2016
Earlier work this paper cites.
Evaluating Cardiomegaly by Radiological Cardiothoracic Ratio as Compared to Conventional Echocardiography
Centurión, O.A., Scavenius, K., Miño, L., Sequeira, O.R., 2017 · 2017
Earlier work this paper cites.
Real time image saliency for black box classifiers
Dabkowski, P., Gal, Y., 2017 · 2017
Cited alongside, same era.
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Fong, R.C., Vedaldi, A., 2017 · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S., 2017 · 2017
Cited alongside, same era.
Pleural effusion imaging: Overview, radiography, computed tomography
Lababede, O., 2017 · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S.M., Allen, P.G., Lee, S.I., 2017 · 2017
Cited alongside, same era.
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Counterfactual Visual Explanations
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S., 2019 · 2019
Later among the works it cites.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., et al., 2019 · 2019
Later among the works it cites.
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A.E., Pollard, T.J., Berkowitz, S.J., Greenbaum, N.R., Lungren, M.P., Deng, C.Y., Mark, R.G., Horng, S., 2019 · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., Aila, T., 2019 · 2019
Later among the works it cites.
Generative Counterfactual Introspection for Explainable Deep Learning
Liu, S., Kailkhura, B., Loveland, D., Han, Y., 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rajpurkar, P., Irvin, J.A., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M., Ng, A., 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., Batra, D., 2017 · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., Kundaje, A., 2017 · 2017
Cited alongside, same era.
Axiomatic Attribution for Deep Networks
Sundararajan, M., Taly, A., Yan, Q., 2017 · 2017
Cited alongside, same era.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A., 2017 · 2017
Cited alongside, same era.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Dhurandhar, A., Chen, P.Y., Luss, R., Tu, C.C., Ting, P., Shanmugam, K., Das, P., 2018 · 2018
Cited alongside, same era.
Towards safe deep learning: Accurately quantifying biomarker uncertainty in neural network predictions
Eaton-Rosen, Z., Bragman, F., Bisdas, S., Ourselin, S., Cardoso, M.J., 2018 · 2018
Cited alongside, same era.
Parafita Martinez, A., Vitria Marca, J., 2019 · 2019
Later among the works it cites.
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization
Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., Pfeiffer, D., 2019 · 2019
Later among the works it cites.
Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists
Rodriguez-Ruiz, A., Lång, K., Gubern-Mérida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M., Andersson, I., Zackrisson, S., Mann, R., Sechopoulos, I., 2019 · 2019
Later among the works it cites.
Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy
Sayres, R., Taly, A., Rahimy, E., Blumer, K., Coz, D., Hammel, N., Krause, J., Narayanaswamy, A., Rastegar, Z., Wu, D., Xu, S., Barb, S., Joseph, A., Shumski, M., Smith, J., Sood, A.B., Corrado, G.S., Peng, L., Webster, D.R., 2019 · 2019
Later among the works it cites.
Explanation by Progressive Exaggeration
Singla, S., Pollack, B., Chen, J., Batmanghelich, K., 2019 · 2019
Later among the works it cites.
Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition
Winkler, J., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., Haenssle, H., 2019 · 2019
Later among the works it cites.
Deep neural network or dermatologist?
Young, K., Booth, G., Simpson, B., Dutton, R., Shrapnel, S., 2019 · 2019
Later among the works it cites.
Explaining image classifiers by removing input features using generative models
Agarwal, C., Nguyen, A., 2020 · 2020
Later among the works it cites.
AI for radiographic COVID-19 detection selects shortcuts over signal
DeGrave, A.J., Janizek, J.D., Lee, S.I., 2020 · 2020
Later among the works it cites.
Is It Time to Get Rid of Black Boxes and Cultivate Trust in AI?
Gastounioti, A., Kontos, D., 2020 · 2020
Later among the works it cites.
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
Larrazabal, A., Nieto, N., Peterson, V., Milone, D., Ferrante, E., 2020 · 2020
Later among the works it cites.
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Mothilal, R.K., Sharma, A., Tan, C., 2020 · 2020
Later among the works it cites.
Scientific Discovery by Generating Counterfactuals using Image Translation
Narayanaswamy, A., Venugopalan, S., Webster, D.R., Peng, L., Corrado, G.S., Ruamviboonsuk, P., Bavishi, P., Brenner, M., Nelson, P.C., Varadarajan, A.V., 2020 · 2020
Later among the works it cites.
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., Ré, C., 2020 · 2020
Later among the works it cites.
Should health care demand interpretable artificial intelligence or accept “black Box" Medicine?
Wang, F., Kaushal, R., Khullar, D., 2020 · 2020
Later among the works it cites.
SCOUT: Self-Aware Discriminant Counterfactual Explanations
Wang, P., Vasconcelos, N., 2020 · 2020
Later among the works it cites.
Gifsplanation via latent shift: A simple autoencoder approach to counterfactual generation for chest x-rays
Cohen, J.P., Brooks, R., En, S., Zucker, E., Pareek, A., Lungren, M.P., Chaudhari, A., 2021 · 2021
Closest in time.
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
Seah, J.C., Tang, C.H., Buchlak, Q.D., Holt, X.G., Wardman, J.B., Aimoldin, A., Esmaili, N., Ahmad, H., Pham, H., Lambert, J.F., et al., 2021 · 2021
Closest in time.
Using causal analysis for conceptual deep learning explanation
Singla, S., Wallace, S., Triantafillou, S., Batmanghelich, K., 2021 · 2021
Closest in time.