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pro vyhledávání: '"Nicolson P"'
Concept-based interpretability methods are a popular form of explanation for deep learning models which provide explanations in the form of high-level human interpretable concepts. These methods typically find concept activation vectors (CAVs) using
Externí odkaz:
http://arxiv.org/abs/2408.08652
The Shared Task on Large-Scale Radiology Report Generation (RRG24) aims to expedite the development of assistive systems for interpreting and reporting on chest X-ray (CXR) images. This task challenges participants to develop models that generate the
Externí odkaz:
http://arxiv.org/abs/2408.03500
Clinical documentation is an important aspect of clinicians' daily work and often demands a significant amount of time. The BioNLP 2024 Shared Task on Streamlining Discharge Documentation (Discharge Me!) aims to alleviate this documentation burden by
Externí odkaz:
http://arxiv.org/abs/2407.02723
This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Traditionally, CXR report generation relies solely on CXR images and limited radiology data, ov
Externí odkaz:
http://arxiv.org/abs/2406.13181
Recent interpretability methods propose using concept-based explanations to translate the internal representations of deep learning models into a language that humans are familiar with: concepts. This requires understanding which concepts are present
Externí odkaz:
http://arxiv.org/abs/2404.03713
Autor:
Casper, Stephen, Yun, Jieun, Baek, Joonhyuk, Jung, Yeseong, Kim, Minhwan, Kwon, Kiwan, Park, Saerom, Moore, Hayden, Shriver, David, Connor, Marissa, Grimes, Keltin, Nicolson, Angus, Tagade, Arush, Rumbelow, Jessica, Nguyen, Hieu Minh, Hadfield-Menell, Dylan
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying convolutional neural networks (CNNs) at the ImageNet scale. The object
Externí odkaz:
http://arxiv.org/abs/2404.02949
Autor:
Huijben, Evi M. C., Terpstra, Maarten L., Galapon, Arthur Jr., Pai, Suraj, Thummerer, Adrian, Koopmans, Peter, Afonso, Manya, van Eijnatten, Maureen, Gurney-Champion, Oliver, Chen, Zeli, Zhang, Yiwen, Zheng, Kaiyi, Li, Chuanpu, Pang, Haowen, Ye, Chuyang, Wang, Runqi, Song, Tao, Fan, Fuxin, Qiu, Jingna, Huang, Yixing, Ha, Juhyung, Park, Jong Sung, Alain-Beaudoin, Alexandra, Bériault, Silvain, Yu, Pengxin, Guo, Hongbin, Huang, Zhanyao, Li, Gengwan, Zhang, Xueru, Fan, Yubo, Liu, Han, Xin, Bowen, Nicolson, Aaron, Zhong, Lujia, Deng, Zhiwei, Müller-Franzes, Gustav, Khader, Firas, Li, Xia, Zhang, Ye, Hémon, Cédric, Boussot, Valentin, Zhang, Zhihao, Wang, Long, Bai, Lu, Wang, Shaobin, Mus, Derk, Kooiman, Bram, Sargeant, Chelsea A. H., Henderson, Edward G. A., Kondo, Satoshi, Kasai, Satoshi, Karimzadeh, Reza, Ibragimov, Bulat, Helfer, Thomas, Dafflon, Jessica, Chen, Zijie, Wang, Enpei, Perko, Zoltan, Maspero, Matteo
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density
Externí odkaz:
http://arxiv.org/abs/2403.08447
Autor:
Kavanagh, Seán R., Squires, Alexander G., Nicolson, Adair, Mosquera-Lois, Irea, Ganose, Alex M., Zhu, Bonan, Brlec, Katarina, Walsh, Aron, Scanlon, David O.
Publikováno v:
Journal of Open Source Software (2024), 9(96), 6433
Defects are a universal feature of crystalline solids, dictating the key properties and performance of many functional materials. Given their crucial importance yet inherent difficulty in measuring experimentally, computational methods (such as DFT a
Externí odkaz:
http://arxiv.org/abs/2403.08012
Autor:
Goedhart, S., Cotton, W. D., Camilo, F., Thompson, M. A., Umana, G., Bietenholz, M., Woudt, P. A., Anderson, L. D., Bordiu, C., Buckley, D. A. H., Buemi, C. S., Bufano, F., Cavallaro, F., Chen, H., Chibueze, J. O., Egbo, D., Frank, B. S., Hoare, M. G., Ingallinera, A., Irabor, T., Kraan-Korteweg, R. C., Kurapati, S., Leto, P., Loru, S., Mutale, M., Obonyo, W. O., Plavin, A., Rajohnson, S. H. A., Rigby, A., Riggi, S., Seidu, M., Serra, P., Smart, B. M., Stappers, B. W., Steyn, N., Surnis, M., Trigilio, C., Williams, G. M., Abbott, T. D., Adam, R. M., Asad, K. M. B., Baloyi, T., Bauermeister, E. F., Bennet, T. G. H., Bester, H., Botha, A. G., Brederode, L. R. S., Buchner, S., Burger, J. P., Cheetham, T., Cloete, K., de Villiers, M. S., de Villiers, D. I. L., Toit, L. J. du, Esterhuyse, S. W. P., Fanaroff, B. L., Fourie, D. J., Gamatham, R. R. G., Gatsi, T. G., Geyer, M., Gouws, M., Gumede, S. C., Heywood, I., Hokwana, A., Hoosen, S. W., Horn, D. M., Horrell, L. M. G., Hugo, B. V., Isaacson, A. I., Józsa, G. I. G., Jonas, J. L., Jordaan, J. D. B. L., Joubert, A. F., Julie, R. P. M., Kapp, F. B., Kriek, N., Kriel, H., Krishnan, V. K., Kusel, T. W., Legodi, L. S., Lehmensiek, R., Lord, R. T., Macfarlane, P. S., Magnus, L. G., Magozore, C., Main, J. P. L., Malan, J. A., Manley, J. R., Marais, S. J., Maree, M. D. J., Martens, A., Maruping, P., McAlpine, K., Merry, B. C., Mgodeli, M., Millenaar, R. P., Mokone, O. J., Monama, T. E., New, W. S., Ngcebetsha, B., Ngoasheng, K. J., Nicolson, G. D., Ockards, M. T., Oozeer, N., Passmoor, S. S., Patel, A. A., Peens-Hough, A., Perkins, S. J., Ramaila, A. J. T., Ratcliffe, S. M., Renil, R., Richter, L. L., Salie, S., Sambu, N., Schollar, C. T. G., Schwardt, L. C., Schwartz, R. L., Serylak, M., Siebrits, R., Sirothia, S. K., Slabber, M. J., Smirnov, O. M., Tiplady, A. J., van Balla, T. J., van der Byl, A., Van Tonder, V., Venter, A. J., Venter, M., Welz, M. G., Williams, L. P.
We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251\deg $\le l \le$ 358\deg and 2\deg $\le l \le$ 61\deg at $|b| \le 1.5\deg $). SMGPS is the largest, most sensitive and hig
Externí odkaz:
http://arxiv.org/abs/2312.07275
Autor:
Corinna Smith, Alexandra Lautarescu, Tony Charman, Jennifer Crosbie, Russell J. Schachar, Alana Iaboni, Stelios Georgiades, Robert Nicolson, Elizabeth Kelley, Muhammad Ayub, Jessica Jones, Paul D. Arnold, Jason P. Lerch, Evdokia Anagnostou, Azadeh Kushki
Publikováno v:
Molecular Autism, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Background Very large sample sizes are often needed to capture heterogeneity in autism, necessitating data sharing across multiple studies with diverse assessment instruments. In these cases, data harmonization can be a critical tool for der
Externí odkaz:
https://doaj.org/article/682693b523ba4c2da9ae0ff332b6cd1c