1. “Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data”
by Alzubaidi, L.; Al-Amidie, M.; Al-Asadi, A.; Humaidi, A. J.; Al-Shamma, O.; Fadhel, M. A.; Zhang, J. L.; Santamaria, J. and Duan, Y.
Cancers 2021, 13(7), 1590; https://doi.org/10.3390/cancers13071590
Available online: https://www.mdpi.com/2072-6694/13/7/1590 

2. “Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging”
by Ayana, G.; Dese, K. and Choe, S. W.
Cancers 2021, 13(4), 738; https://doi.org/10.3390/cancers13040738
Available online: https://www.mdpi.com/2072-6694/13/4/738

3. “Structured Reporting of Rectal Cancer Staging and Restaging: A Consensus Proposal”
by Granata, V.; Caruso, D.; Grassi, R.; Cappabianca, S.; Reginelli, A.; Rizzati, R.; Masselli, G.; Golfieri, R.; Rengo, M.; Regge, D.; Lo Re, G.; Pradella, S.; Fusco, R.; Faggioni, L.; Laghi, A.; Miele, V.; Neri, E. and Coppola, F.
Cancers 2021, 13(9), 2135; https://doi.org/10.3390/cancers13092135
Available online: https://www.mdpi.com/2072-6694/13/9/2135

4. “Tumor Mutational Burden as a Potential Biomarker for Immunotherapy in Pancreatic Cancer: Systematic Review and Still-Open Questions”
by Lawlor, R. T.; Mattiolo, P.; Mafficini, A.; Hong, S.-M.; Piredda, M. L.; Taormina, S. V.; Malleo, G.; Marchegiani, G.; Pea, A.; Salvia, R.; Kryklyva, V.; Shin, J. I.; Brosens, L. A.; Milella, M.; Scarpa, A. and Luchini, C.
Cancers 2021, 13(13), 3119; https://doi.org/10.3390/cancers13133119
Available online: https://www.mdpi.com/2072-6694/13/13/3119

5. “OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data”
by Zhang, X.; Xing, Y.; Sun, K. and Guo, Y.
Cancers 2021, 13(12), 3047; https://doi.org/10.3390/cancers13123047
Available online: https://www.mdpi.com/2072-6694/13/12/3047

6. “Identification of Cancer Hub Gene Signatures Associated with Immune-Suppressive Tumor Microenvironment and Ovatodiolide as a Potential Cancer Immunotherapeutic Agent”
by Chen, J.-H.; Wu, A. T. H.; Lawal, B.; Tzeng, D. T. W.; Lee, J.-C.; Ho, C.-L. and Chao, T.-Y.
Cancers 2021, 13(15), 3847; https://doi.org/10.3390/cancers13153847
Available online: https://www.mdpi.com/2072-6694/13/15/3847

7. “Nanopore Sequencing Reveals Global Transcriptome Signatures of Mitochondrial and Ribosomal Gene Expressions in Various Human Cancer Stem-like Cell Populations”
by Witte, K. E.; Hertel, O.; Windmöller, B. A.; Helweg, L. P.; Höving, A. L.; Knabbe, C.; Busche, T.; Greiner, J. F. W.; Kalinowski, J.; Noll, T.; Mertzlufft, F.; Beshay, M.; Pfitzenmaier, J.; Kaltschmidt, B.; Kaltschmidt, C.; Banz-Jansen, C. and Simon, M.
Cancers 2021, 13(5), 1136; https://doi.org/10.3390/cancers13051136
Available online: https://www.mdpi.com/2072-6694/13/5/1136

8. “Unsupervised Flow Cytometry Analysis Allows for an Accurate Identification of Minimal Residual Disease Assessment in Acute Myeloid Leukemia”
by Vial, J. P.; Lechevalier, N.; Lacombe, F.; Dumas, P.-Y.; Bidet, A.; Leguay, T.; Vergez, F.; Pigneux, A. and Béné, M.C.
Cancers 2021, 13(4), 629; https://doi.org/10.3390/cancers13040629
Available online: https://www.mdpi.com/2072-6694/13/4/629

9. “Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue”
by Nishio, M.; Nishio, M.; Jimbo, N. and Nakane, K.
Cancers 2021, 13(6), 1192; https://doi.org/10.3390/cancers13061192
Available online: https://www.mdpi.com/2072-6694/13/6/1192

10. “Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results”
by Camalan, S.; Mahmood, H.; Binol, H.; Araújo, A. L. D.; Santos-Silva, A. R.; Vargas, P. A.; Lopes, M. A.; Khurram, S. A. and Gurcan, M. N.
Cancers 2021, 13(6), 1291; https://doi.org/10.3390/cancers13061291
Available online: https://www.mdpi.com/2072-6694/13/6/1291