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Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Adil Mehmood Khan +6
Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffe…
Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentation
Suruchi Kumari, Aryan Das, Swalpa Kumar Roy +2
Traditional supervised 3D medical image segmentation models need voxel-level annotations, which require huge human effort, time, and cost. Semi-supervised learning (SSL) addresses…
How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification
Ali Jamali, Swalpa Kumar Roy, Danfeng Hong +2
Convolutional Neural Networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification. However, these models requi…
Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?
Pallabi Dutta, Soham Bose, Swalpa Kumar Roy +1
The development of efficient segmentation strategies for medical images has evolved from its initial dependence on Convolutional Neural Networks (CNNs) to the current investigation…
Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation
Wentao Wang, Xi Xiao, Mingjie Liu +5
The accurate segmentation of medical images is crucial for diagnosing and treating diseases. Recent studies demonstrate that vision transformer-based methods have significantly imp…
Reliable or Deceptive? Investigating Gated Features for Smooth Visual Explanations in CNNs
Soham Mitra, Atri Sukul, Swalpa Kumar Roy +2
Deep learning models have achieved remarkable success across diverse domains. However, the intricate nature of these models often impedes a clear understanding of their decision-ma…