Benchmarking CNN Architectures Using Transfer Learning for Lipomatous Tumor Classification: A Comprehensive Study

Siam Tahsin Bhuiyan; Rashedur Rahman; Ashraful Islam

doi:10.1007/978-981-92-2891-1_14

Conference Paper2026

Benchmarking CNN Architectures Using Transfer Learning for Lipomatous Tumor Classification: A Comprehensive Study

Siam Tahsin Bhuiyan, Rashedur Rahman, Ashraful Islam

Advances and Trends in Artificial Intelligence. Theory and Applications: 39th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2026, Kuala Lumpur, Malaysia, July 6-8, 2026, Proceedings, Part II

In: Advances and Trends in Artificial Intelligence. Theory and Applications

Springer Nature Singapore, pp. 171, ISBN: 9789819228904

DOI: 10.1007/978-981-92-2891-1_14 URL

Keywords

Convolutional neural networkBenchmarkingTransfer of learningMagnetic resonance imagingDeep learningPattern recognition (psychology)Artificial neural networkContextual image classification

CCDS Authors

Ashraful Islam, PhD

Center Director, CCDS
Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

References

1.Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2016). Deep Residual Learning for Image Recognition. , 770–778[10.1109/cvpr.2016.90]
2.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255[10.1109/cvpr.2009.5206848]
3.Gao Huang, Zhuang Liu, Laurens Van Der Maaten, Kilian Q. Weinberger. (2017). Densely Connected Convolutional Networks. , 2261–2269[10.1109/cvpr.2017.243]
4.Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, Zbigniew Wojna. (2016). Rethinking the Inception Architecture for Computer Vision. , 2818–2826[10.1109/cvpr.2016.308]
5.Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. , 4510–4520[10.1109/cvpr.2018.00474]
6.Dinggang Shen, Guorong Wu, Heung-Il Suk. (2017). Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19(1), 221–248[10.1146/annurev-bioeng-071516-044442]
7.Xuxin Chen, Ximin Wang, Ke Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444[10.1016/j.media.2022.102444]
8.S. Suganyadevi, V. Seethalakshmi, K. Balasamy. (2021). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), 19–38[10.1007/s13735-021-00218-1]
9.Alex Thomas John Lee, Khin Thway, Paul H. Huang, Robin Lewis Jones. (2017). Clinical and Molecular Spectrum of Liposarcoma. Journal of Clinical Oncology, 36(2), 151–159[10.1200/jco.2017.74.9598]
10.Mélanie Brisson, Takeshi Kashima, David Delaney, Roberto Tirabosco, Andrew Clarke, Suzie Cro, Adrienne M. Flanagan, Paul O’Donnell. (2012). MRI characteristics of lipoma and atypical lipomatous tumor/well-differentiated liposarcoma: retrospective comparison with histology and MDM2 gene amplification. Skeletal Radiology, 42(5), 635–647[10.1007/s00256-012-1517-z]
11.Shunli Liu, Weikai Sun, Shifeng Yang, Lisha Duan, Chencui Huang, Jingxu Xu, Feng Hou, Dapeng Hao, Tengbo Yu, Hexiang Wang. (2021). Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. European Radiology, 32(2), 793–805[10.1007/s00330-021-08221-0]
12.Fernando Navarro, Hendrik Dapper, Rebecca Asadpour, Carolin Knebel, Matthew B. Spraker, Vincent Schwarze, Stephanie K. Schaub, Nina A. Mayr, Katja Specht, Henry C. Woodruff, Philippe Lambin, Alexandra S. Gersing, Matthew J. Nyflot, Bjoern H. Menze, Stephanie E. Combs, Jan C. Peeken. (2021). Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging. Cancers, 13(12), 2866[10.3390/cancers13122866]
13.Ieva Malinauskaite, Jeremy Hofmeister, Simon Burgermeister, Angeliki Neroladaki, Marion Hamard, Xavier Montet, Sana Boudabbous. (2020). Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists. Sarcoma, 2020, 1–9[10.1155/2020/7163453]
14.Guillaume Fradet, Reina Ayde, Hugo Bottois, Mohamed El Harchaoui, Wassef Khaled, Jean-Luc Drapé, Frank Pilleul, Amine Bouhamama, Olivier Beuf, Benjamin Leporq. (2022). Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning. European Radiology Experimental, 6(1), 41[10.1186/s41747-022-00295-9]
15.Salvatore Gitto, Matteo Interlenghi, Renato Cuocolo, Christian Salvatore, Vincenzo Giannetta, Julietta Badalyan, Enrico Gallazzi, Maria Silvia Spinelli, Mauro Gallazzi, Francesca Serpi, Carmelo Messina, Domenico Albano, Alessio Annovazzi, Vincenzo Anelli, Jacopo Baldi, Alberto Aliprandi, Elisabetta Armiraglio, Antonina Parafioriti, Primo Andrea Daolio, Alessandro Luzzati, Roberto Biagini, Isabella Castiglioni, Luca Maria Sconfienza. (2023). MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities. La radiologia medica, 128(8), 989–998[10.1007/s11547-023-01657-y]
16.Maggie Y. Zhou, Nam Q. Bui, Gregory W. Charville, Kristen N. Ganjoo, Minggui Pan. (2023). Treatment of De-Differentiated Liposarcoma in the Era of Immunotherapy. International Journal of Molecular Sciences, 24(11), 9571[10.3390/ijms24119571]
17.Benjamin Wang, Laetitia Perronne, Christopher Burke, Ronald S. Adler. (2020). Artificial Intelligence for Classification of Soft-Tissue Masses at US. Radiology Artificial Intelligence, 3(1), e200125[10.1148/ryai.2020200125]
18.Elliott J. Yee, Camille L. Stewart, Michael R. Clay, Martin M. McCarter. (2022). Lipoma and Its Doppelganger. Surgical Clinics of North America, 102(4), 637–656[10.1016/j.suc.2022.04.006]
19.Yuhan Yang, Yin Zhou, Chen Zhou, Xuelei Ma. (2022). Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods. Orphanet Journal of Rare Diseases, 17(1), 158[10.1186/s13023-022-02304-x]
20.Anthony Bozzo, Alex Hollingsworth, Subrata Chatterjee, Aditya Apte, Jiawen Deng, Simon Sun, William Tap, Ahmed Aoude, Sahir Bhatnagar, John H. Healey. (2024). A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma. npj Precision Oncology, 8(1), 188[10.1038/s41698-024-00695-7]
21.Haithem Hermessi, Olfa Mourali, Ezzeddine Zagrouba. (2019). Transfer learning with multiple convolutional neural networks for soft tissue sarcoma MRI classification. , 15[10.1117/12.2522765]
22.Minting Zheng, Chenhua Guo, Yifeng Zhu, Xiaoming Gang, Chongyang Fu, Shaowu Wang. (2024). Segmentation model of soft tissue sarcoma based on self-supervised learning. Frontiers in Oncology, 14, 1247396[10.3389/fonc.2024.1247396]
23.Chih-Chieh Liu, Yasser G. Abdelhafez, S. Paran Yap, Francesco Acquafredda, Silvia Schirò, Andrew L. Wong, Dani Sarohia, Cyrus Bateni, Morgan A. Darrow, Michele Guindani, Sonia Lee, Michelle Zhang, Ahmed W. Moawad, Quinn Kwan-Tai Ng, Layla Shere, Khaled M. Elsayes, Roberto Maroldi, Thomas M. Link, Lorenzo Nardo, Jinyi Qi. (2023). AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data. Journal of Imaging Informatics in Medicine, 36(3), 1049–1059[10.1007/s10278-023-00785-1]
24.Martijn P.A. Starmans, Milea J.M. Timbergen, Melissa Vos, Guillaume A. Padmos, Dirk J. Grünhagen, Cornelis Verhoef, Stefan Sleijfer, Geert J.L.H. van Leenders, Florian E. Buisman, Francois E.J.A. Willemssen, Bas Groot Koerkamp, Lindsay Angus, Astrid A.M. van der Veldt, Ana Rajicic, Arlette E. Odink, Michel Renckens, Michail Doukas, Rob A. de Man, Jan N.M. IJzermans, Razvan L. Miclea, Peter B. Vermeulen, Maarten G. Thomeer, Jacob J. Visser, Wiro J. Niessen, Stefan Klein. (2021). The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies. medRxiv[10.1101/2021.08.19.21262238]

← All publications

Benchmarking CNN Architectures Using Transfer Learning for Lipomatous Tumor Classification: A Comprehensive Study

Siam Tahsin Bhuiyan, Rashedur Rahman, Ashraful Islam

In: Advances and Trends in Artificial Intelligence. Theory and Applications

Springer Nature Singapore, pp. 171, ISBN: 9789819228904

References

1.Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2016). Deep Residual Learning for Image Recognition. , 770–778[10.1109/cvpr.2016.90]

2.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255[10.1109/cvpr.2009.5206848]

3.Gao Huang, Zhuang Liu, Laurens Van Der Maaten, Kilian Q. Weinberger. (2017). Densely Connected Convolutional Networks. , 2261–2269[10.1109/cvpr.2017.243]

4.Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, Zbigniew Wojna. (2016). Rethinking the Inception Architecture for Computer Vision. , 2818–2826[10.1109/cvpr.2016.308]

5.Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. , 4510–4520[10.1109/cvpr.2018.00474]

6.Dinggang Shen, Guorong Wu, Heung-Il Suk. (2017). Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19(1), 221–248[10.1146/annurev-bioeng-071516-044442]

7.Xuxin Chen, Ximin Wang, Ke Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444[10.1016/j.media.2022.102444]

8.S. Suganyadevi, V. Seethalakshmi, K. Balasamy. (2021). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), 19–38[10.1007/s13735-021-00218-1]

9.Alex Thomas John Lee, Khin Thway, Paul H. Huang, Robin Lewis Jones. (2017). Clinical and Molecular Spectrum of Liposarcoma. Journal of Clinical Oncology, 36(2), 151–159[10.1200/jco.2017.74.9598]

10.Mélanie Brisson, Takeshi Kashima, David Delaney, Roberto Tirabosco, Andrew Clarke, Suzie Cro, Adrienne M. Flanagan, Paul O’Donnell. (2012). MRI characteristics of lipoma and atypical lipomatous tumor/well-differentiated liposarcoma: retrospective comparison with histology and MDM2 gene amplification. Skeletal Radiology, 42(5), 635–647[10.1007/s00256-012-1517-z]

11.Shunli Liu, Weikai Sun, Shifeng Yang, Lisha Duan, Chencui Huang, Jingxu Xu, Feng Hou, Dapeng Hao, Tengbo Yu, Hexiang Wang. (2021). Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. European Radiology, 32(2), 793–805[10.1007/s00330-021-08221-0]

12.Fernando Navarro, Hendrik Dapper, Rebecca Asadpour, Carolin Knebel, Matthew B. Spraker, Vincent Schwarze, Stephanie K. Schaub, Nina A. Mayr, Katja Specht, Henry C. Woodruff, Philippe Lambin, Alexandra S. Gersing, Matthew J. Nyflot, Bjoern H. Menze, Stephanie E. Combs, Jan C. Peeken. (2021). Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging. Cancers, 13(12), 2866[10.3390/cancers13122866]

13.Ieva Malinauskaite, Jeremy Hofmeister, Simon Burgermeister, Angeliki Neroladaki, Marion Hamard, Xavier Montet, Sana Boudabbous. (2020). Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists. Sarcoma, 2020, 1–9[10.1155/2020/7163453]

14.Guillaume Fradet, Reina Ayde, Hugo Bottois, Mohamed El Harchaoui, Wassef Khaled, Jean-Luc Drapé, Frank Pilleul, Amine Bouhamama, Olivier Beuf, Benjamin Leporq. (2022). Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning. European Radiology Experimental, 6(1), 41[10.1186/s41747-022-00295-9]

15.Salvatore Gitto, Matteo Interlenghi, Renato Cuocolo, Christian Salvatore, Vincenzo Giannetta, Julietta Badalyan, Enrico Gallazzi, Maria Silvia Spinelli, Mauro Gallazzi, Francesca Serpi, Carmelo Messina, Domenico Albano, Alessio Annovazzi, Vincenzo Anelli, Jacopo Baldi, Alberto Aliprandi, Elisabetta Armiraglio, Antonina Parafioriti, Primo Andrea Daolio, Alessandro Luzzati, Roberto Biagini, Isabella Castiglioni, Luca Maria Sconfienza. (2023). MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities. La radiologia medica, 128(8), 989–998[10.1007/s11547-023-01657-y]

16.Maggie Y. Zhou, Nam Q. Bui, Gregory W. Charville, Kristen N. Ganjoo, Minggui Pan. (2023). Treatment of De-Differentiated Liposarcoma in the Era of Immunotherapy. International Journal of Molecular Sciences, 24(11), 9571[10.3390/ijms24119571]

17.Benjamin Wang, Laetitia Perronne, Christopher Burke, Ronald S. Adler. (2020). Artificial Intelligence for Classification of Soft-Tissue Masses at US. Radiology Artificial Intelligence, 3(1), e200125[10.1148/ryai.2020200125]

18.Elliott J. Yee, Camille L. Stewart, Michael R. Clay, Martin M. McCarter. (2022). Lipoma and Its Doppelganger. Surgical Clinics of North America, 102(4), 637–656[10.1016/j.suc.2022.04.006]

19.Yuhan Yang, Yin Zhou, Chen Zhou, Xuelei Ma. (2022). Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods. Orphanet Journal of Rare Diseases, 17(1), 158[10.1186/s13023-022-02304-x]

20.Anthony Bozzo, Alex Hollingsworth, Subrata Chatterjee, Aditya Apte, Jiawen Deng, Simon Sun, William Tap, Ahmed Aoude, Sahir Bhatnagar, John H. Healey. (2024). A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma. npj Precision Oncology, 8(1), 188[10.1038/s41698-024-00695-7]

21.Haithem Hermessi, Olfa Mourali, Ezzeddine Zagrouba. (2019). Transfer learning with multiple convolutional neural networks for soft tissue sarcoma MRI classification. , 15[10.1117/12.2522765]

22.Minting Zheng, Chenhua Guo, Yifeng Zhu, Xiaoming Gang, Chongyang Fu, Shaowu Wang. (2024). Segmentation model of soft tissue sarcoma based on self-supervised learning. Frontiers in Oncology, 14, 1247396[10.3389/fonc.2024.1247396]

23.Chih-Chieh Liu, Yasser G. Abdelhafez, S. Paran Yap, Francesco Acquafredda, Silvia Schirò, Andrew L. Wong, Dani Sarohia, Cyrus Bateni, Morgan A. Darrow, Michele Guindani, Sonia Lee, Michelle Zhang, Ahmed W. Moawad, Quinn Kwan-Tai Ng, Layla Shere, Khaled M. Elsayes, Roberto Maroldi, Thomas M. Link, Lorenzo Nardo, Jinyi Qi. (2023). AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data. Journal of Imaging Informatics in Medicine, 36(3), 1049–1059[10.1007/s10278-023-00785-1]

24.Martijn P.A. Starmans, Milea J.M. Timbergen, Melissa Vos, Guillaume A. Padmos, Dirk J. Grünhagen, Cornelis Verhoef, Stefan Sleijfer, Geert J.L.H. van Leenders, Florian E. Buisman, Francois E.J.A. Willemssen, Bas Groot Koerkamp, Lindsay Angus, Astrid A.M. van der Veldt, Ana Rajicic, Arlette E. Odink, Michel Renckens, Michail Doukas, Rob A. de Man, Jan N.M. IJzermans, Razvan L. Miclea, Peter B. Vermeulen, Maarten G. Thomeer, Jacob J. Visser, Wiro J. Niessen, Stefan Klein. (2021). The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies. medRxiv[10.1101/2021.08.19.21262238]