Recent advancements in foundation models, such as the Segment Anything Model (SAM), have significantly impacted medical image segmentation, especially in retinal imaging, where precise segmentation is vital for diagnosis. Despite this progress, current methods face critical challenges: 1) modality ambiguity in textual disease descriptions, 2) a continued reliance on manual prompting for SAM-based workflows, and 3) a lack of a unified framework, with most methods being modalityand task-specific. To overcome these hurdles, we propose CLIP-unified Auto-Prompt Segmentation (CLAPS), a novel method for unified segmentation across diverse tasks and modalities in retinal imaging. Our approach begins by pre-training a CLIP-based image encoder on a large, multi-modal retinal dataset to handle data scarcity and distribution imbalance. We then leverage GroundingDINO to automatically generate spatial bounding box prompts by detecting local lesions. To unify tasks and resolve ambiguity, we use text prompts enhanced with a unique “modality signature” for each imaging modality. Ultimately, these automated textual and spatial prompts guide SAM to execute precise segmentation, creating a fully automated and unified pipeline. Extensive experiments on 12 diverse datasets across 11 critical segmentation categories show that CLAPS achieves performance on par with specialized expert models while surpassing existing benchmarks across most metrics, demonstrating its broad generalizability as a foundation model.

The utilization of longitudinal datasets for glaucoma progression prediction offers a compelling approach to support early therapeutic interventions. Predominant methodologies in this domain have primarily focused on the direct prediction of glaucoma stage labels from longitudinal datasets. However, such methods may not adequately encapsulate the nuanced developmental trajectory of the disease. To enhance the diagnostic acumen of medical practitioners, we propose a novel diffusion-based model to predict prospective images by extrapolating from existing longitudinal fundus images of patients. The methodology delineated in this study distinctively leverages sequences of images as inputs. Subsequently, a time-aligned mask is employed to select a specific year for image generation. During the training phase, the time-aligned mask resolves the issue of irregular temporal intervals in longitudinal image sequence sampling. Additionally, we utilize a strategy of randomly masking a frame in the sequence to establish the ground truth. This methodology aids the network in continuously acquiring knowledge regarding the internal relationships among the sequences throughout the learning phase. Moreover, the introduction of textual labels is instrumental in categorizing images generated within the sequence. The empirical findings from the conducted experiments indicate that our proposed model not only effectively generates longitudinal data but also significantly improves the precision of downstream classification tasks.

We propose a novel computationally efficient hierarchical dictionary learning (HDL) approach for data-driven unmixing and functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. It is shown that by simultaneously exploiting the sparsity of the spatial brain maps and the incoherence among their evolution in time or task functions, one can achieve better performance while overcoming the drawbacks of existing approaches. The task functions constituting the dictionary, are learned using a hierarchical subset selection approach. Here, to enforce incoherence among atoms, any new atom is selected from suitable training candidates if it does not lie in the column span of past selected atoms. Also, since the sparsity of spatial maps is generally unknown and affected due to acquisition artifacts, HDL doesn't make use of an implicit sparse coding stage while dictionary update. This makes HDL a very fast and efficient data-driven approach for fMRI analysis. Experimental results on synthetic and real fMRI datasets provide compelling evidences that HDL performs better than existing state-of-the-art methods.

In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low-rank plus sparse tensor decomposition problem, we propose an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the state-of-the-art reconstruction methods.

Identifying the presence of Anti-Nuclear Antibody (ANA) in Human Epithelial Type-2 (HEp-2) cells via Indirect Immunofluoresence (IIF) images is commonly used to detect various diseases in clinical pathology tests. The main task at hand is the classification of cells into different categories by observing their staining patterns. However, when performed manually, this method is time and labour intensive. Also, as IIF analysis is still subjective, medical doctors have not been able to get satisfactory accuracy rates on the classification task. Pattern recognition techniques have been introduced, but performance of current systems in literature are not satisfactory as they use a common set of descriptors to describe images from all classes and perform classification in one go. Here, we propose a system based on hierarchical classification that takes into account discriminative features for classification purpose. A new texture descriptor based on change of curvature of the parametric from of image intensity is proposed. We have also introduced a model adaptation technique using non-linear transformation functions to make the classifiers more robust.