40 citations · 143 across the 13 of their papers we have counts for
7 papers · 1 filter
CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets
Mominul Islam, Hasib Zunair, Nabeel Mohammed
Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation.…
LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification
Abu Adnan Sadi, Labib Chowdhury, Nusrat Jahan +4
With advances in digital technology, the classification of medical images has become a crucial step for image-based clinical decision support systems. Automatic medical image class…
Knowledge Distillation approach towards Melanoma Detection
Md. Shakib Khan, Kazi Nabiul Alam, Abdur Rab Dhruba +2
Melanoma is regarded as the most threatening among all skin cancers. There is a pressing need to build systems which can aid in the early detection of melanoma and enable timely tr…
LILA-BOTI : Leveraging Isolated Letter Accumulations By Ordering Teacher Insights for Bangla Handwriting Recognition
Md. Ismail Hossain, Mohammed Rakib, Sabbir Mollah +2
Word-level handwritten optical character recognition (OCR) remains a challenge for morphologically rich languages like Bangla. The complexity arises from the existence of a large n…
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout
Md. Istiak Hossain Shihab, Nazia Tasnim, Hasib Zunair +2
Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout. The task is challenging due to the real-world scenario of…
Dynamic Rectification Knowledge Distillation
Fahad Rahman Amik, Ahnaf Ismat Tasin, Silvia Ahmed +2
Knowledge Distillation is a technique which aims to utilize dark knowledge to compress and transfer information from a vast, well-trained neural network (teacher model) to a smalle…
ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans
Hasib Zunair, Aimon Rahman, Nabeel Mohammed
Pretraining has sparked groundswell of interest in deep learning workflows to learn from limited data and improve generalization. While this is common for 2D image classification t…