Explainable deep learning methods in medical image classification: A survey
The remarkable success of deep learning has prompted interest in its application to medical
imaging diagnosis. Even though state-of-the-art deep learning models have achieved …
imaging diagnosis. Even though state-of-the-art deep learning models have achieved …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Skin cancer diagnosis: Leveraging deep hidden features and ensemble classifiers for early detection and classification
Problem Cancer is a deadly disease that requires better diagnostics. Early detection
improves skin cancer survival. Due to skin lesion dissimilarity, automated image …
improves skin cancer survival. Due to skin lesion dissimilarity, automated image …
Nurvid: A large expert-level video database for nursing procedure activity understanding
The application of deep learning to nursing procedure activity understanding has the
potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing …
potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing …
Mica: Towards explainable skin lesion diagnosis via multi-level image-concept alignment
Black-box deep learning approaches have showcased significant potential in the realm of
medical image analysis. However, the stringent trustworthiness requirements intrinsic to the …
medical image analysis. However, the stringent trustworthiness requirements intrinsic to the …
Concept-based Lesion Aware Transformer for Interpretable Retinal Disease Diagnosis
Existing deep learning methods have achieved remarkable results in diagnosing retinal
diseases, showcasing the potential of advanced AI in ophthalmology. However, the black …
diseases, showcasing the potential of advanced AI in ophthalmology. However, the black …
From hope to safety: Unlearning biases of deep models via gradient penalization in latent space
Deep Neural Networks are prone to learning spurious correlations embedded in the training
data, leading to potentially biased predictions. This poses risks when deploying these …
data, leading to potentially biased predictions. This poses risks when deploying these …
Prompt-driven latent domain generalization for medical image classification
Deep learning models for medical image analysis easily suffer from distribution shifts
caused by dataset artifact bias, camera variations, differences in the imaging station, etc …
caused by dataset artifact bias, camera variations, differences in the imaging station, etc …
SLIM: Spuriousness Mitigation with Minimal Human Annotations
Recent studies highlight that deep learning models often learn spurious features mistakenly
linked to labels, compromising their reliability in real-world scenarios where such …
linked to labels, compromising their reliability in real-world scenarios where such …
[HTML][HTML] Artificial Intelligence in the Non-Invasive Detection of Melanoma
Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin
cancer is typically classified as melanoma or non-melanoma skin cancer. Although …
cancer is typically classified as melanoma or non-melanoma skin cancer. Although …