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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Sarwar, F., Garrido, N., Sebastião, P. & Margarida Silveira (2025). Enhanced Multiple Instance Learning for Breast Cancer Detection in Mammography: Adaptive Patching, Advanced Pooling, and Deep Supervision. In 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (pp. 1-6). Copenhagen, Denmark: IEEE.
Exportar Referência (IEEE) 
F. Sarwar et al.,  "Enhanced Multiple Instance Learning for Breast Cancer Detection in Mammography: Adaptive Patching, Advanced Pooling, and Deep Supervision", in 2025 47th Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark, IEEE, 2025, pp. 1-6
Exportar BibTeX 
@inproceedings{sarwar2025_1766199808241,
	author = "Sarwar, F. and Garrido, N. and Sebastião, P. and Margarida Silveira",
	title = "Enhanced Multiple Instance Learning for Breast Cancer Detection in Mammography: Adaptive Patching, Advanced Pooling, and Deep Supervision",
	booktitle = "2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
	year = "2025",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/EMBC58623.2025.11254317",
	pages = "1-6",
	publisher = "IEEE",
	address = "Copenhagen, Denmark",
	organization = "Eng Med Biol Soc",
	url = "https://embc.embs.org/2025/"
}
Exportar RIS 
TY  - CPAPER
TI  - Enhanced Multiple Instance Learning for Breast Cancer Detection in Mammography: Adaptive Patching, Advanced Pooling, and Deep Supervision
T2  - 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
AU  - Sarwar, F.
AU  - Garrido, N.
AU  - Sebastião, P.
AU  - Margarida Silveira
PY  - 2025
SP  - 1-6
SN  - 2375-7477
DO  - 10.1109/EMBC58623.2025.11254317
CY  - Copenhagen, Denmark
UR  - https://embc.embs.org/2025/
AB  - This paper addresses the challenge of weakly su-
pervised learning for breast cancer detection in mammography
by introducing an Enhanced Embedded Space MI-Net model
with deep supervision. The framework integrated adaptive
patch creation, convolution feature extraction, and pooling
methods -max, mean, log-sum-expo, attention, and gated atten-
tion pooling - evaluated in three MIL models, Instance Space
mi-Net, Embedded Space MI-Net and Enhanced Embedded
Space MI-Net. A key contribution is the incorporation of deep
supervision, improving feature learning across network layers
and enhancing bag-level classification performance. Experimen-
tal results on the CBIS / DDSM dataset demonstrate that the
Enhanced MI-Net model achieves the highest AUC of 86% with
attention pooling. This work addresses the gap in leveraging
MIL techniques for high-resolution medical imaging without
requiring detailed annotations, offering a robust and scalable
solution for breast cancer detection.
ER  -