Addressing fairness issues in deep learning-based medical image analysis: a systematic review.

Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA.

IGU-Aug: Information-guided unsupervised augmentation and pixel-wise contrastive learning for medical image analysis.

Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel-wise contrastive learning mainly helps with pixel-wise dense prediction tasks. The counter-part to an instance in instance-level CL is a pixel, along with its neighboring context, in pixel-wise CL.

Which images to label for few-shot medical image analysis?

The success of deep learning methodologies hinges upon the availability of meticulously labeled extensive datasets. However, when dealing with medical images, the annotation process for such abundant training data often necessitates the involvement of experienced radiologists, thereby consuming their limited time resources. In order to alleviate this burden, few-shot learning approaches have been developed, which manage to achieve competitive performance levels with only several labeled images.

Adversarial Medical Image with Hierarchical Feature Hiding.

Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which optimize the classification logits, are easy to distinguish in the feature space, resulting in accurate reactive defenses. To better understand this phenomenon and reassess the reliability of the reactive defenses for medical AEs, we thoroughly investigate the characteristic of conventional medical AEs.

Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model.

. In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on a single dataset, this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark location.

The autopsy study of 553 cases of sudden cardiac death in Chinese adults.

Despite a recent epidemiological study reporting a lower incidence of sudden cardiac death (SCD) in China as compared with that in Western countries, the exact causes of SCD are still unknown. Using a uniform review protocol and diagnostic criteria, a retrospective autopsy study identified 553 cases of SCD in 14,487 consecutive autopsies from eight regions in China representing different geographic and population features. Their ages ranged from 18 to 80 years (median 43.

[Pathologic changes of the cardiac conduction system in 12 patients with abnormal ECG].

Objective: To investigate the relationship between abnormal ECG and pathologic changes in the cardiac conduction system (CCS).

Method: Pathological changes of the CCS in 12 cases with abnormal ECG out of 16 pre-death ECG were observed.

Results: (1) Among 7 cases of sudden cardiac death, ECG monitoring recorded bradyarrhythmia in 6 cases, tachyarrhythmia 6 cases, bradycardia-tachycardia syndrome 2 cases, conduction block 6 cases, atrial premature beats 6 cases, ventricular premature beats 6 cases, and ST-T changes 4 cases.

[Traumatic cerebral infarction: a histopathological study of 17 cases].

Objective: To assess the morphologic changes in traumatic cerebral infarction and to discuss its mechanism.

Methods: Specimens from seventeen cases of cerebral infarction were selected from 81 patients with severe brain injury, and subject to routine gross and histological examinations.

Results: (1) The cerebral infarction in all cases was hemorrhagic in nature with a wedged or irregular shape upon gross inspection.

[Morphological changes of tissues after extensive soft tissue injury].

Objective: To study the pathological morphological changes for diagnosing the cause of death of extensive soft tissue injury or crush syndrome.

Methods: The tissues were stained by HE and IHC.

Results: (1) The Mb positive rate was 60%, 75%, 95% respectively.