Hướng nghiên cứu tiếp theo của luận án nhằm để phát triển công trình để có thể đưa vào ứng dụng trong thực tiễn như sau:
1. Tiếp tục nghiên cứu các mô hình học sâu cho phân vùng polyp để có thể xây dựng được mô hình có hiệu năng đủ tốt và có chi phí tính toán vừa phải phù hợp với hệ thống phần cứng thực tế khi triển khai ứng dụng.
2. Nghiên cứu cải tiến phương pháp xác định siêu tham số tối ưu của hàm mất mát không đối xứng kết hợp để giảm thiểu công sức cho huấn luyện mô hình tìm kiếm siêu tham số tối ưu.
3. Nghiên cứu, thử nghiệm các bộ mã hóa khác cho mạng UNet trong mô hình học tự giám sát, từ đó đưa ra bộ mã hóa phù hợp cho độ chính xác phân vùng polyp cao hơn.
DANH MỤC CÁC CÔNG TRÌNH KHOA HỌC ĐÃ CÔNG BỐ
[CT1]. Le Thi Thu Hong, Nguyen Chi Thanh, and Tran Quoc Long, “Polyp segmentation in colonoscopy images using ensembles of u-nets with efficientnet and asymmetric similarity loss function,” in 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), IEEE, pp.1–6, 2020.
[CT2]. Lê Thị Thu Hồng, Nguyễn Chí Thành, Phạm Thu Hương, Nguyễn Sinh Huy, Nguyễn Văn Đức, Nguyễn Thành Trung, “Tăng cường dữ liệu huấn luyện cho hệ thống học sâu phân vùng polyp trên ảnh nội soi đại tràng”, Tạp chí Nghiên cứu Khoa học và Công nghệ quân sự, số Đặc san Hội thảo Quốc gia FEE, tr. 447-454, 10-2020.
[CT3]. Le Thi Thu Hong, Nguyen Chi Thanh, and Tran Quoc Long, "CRF-EfficientUNet: an improved UNet framework for polyp segmentation in colonoscopy images with combined asymmetric loss function and CRF-RNN layer,” IEEE Access, vol. 9, pp. 156987 - 157001, 2021 (SCIE Q1, IF: 3,367).
[CT4]. Lê Thị Thu Hồng, Nguyễn Chí Thành, Nguyễn Đức Hạnh, Trịnh Tiến Lương, Phạm Duy Thái, Ngô Văn Quân “Colonoscopy Image Classification Using Self-Supervised Visual Feature Learning”. Section on Computer Science and Control Engineering, Journal of Military science and technology, Sepecial Issue No.5, pp. 3-13, 12-2021.
[CT5]. Le Thi Thu Hong, Nguyen Chi Thanh and Tran Quoc Long, "Self- supervised Visual Feature Learning for Polyp Segmentation in Colonoscopy Images Using Image Reconstruction as Pretext Task" 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), 2021, pp. 254-259, doi: 10.1109/NICS54270.2021.9701580.
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