How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.Comment: Early accepted to MICCAI 202

Deciphering the origin and therapeutic targets of cancer of unknown primary: a case report that illustrates the power of integrative whole-exome and transcriptome sequencing analysis

Cancer of unknown primary (CUP) represents a significant diagnostic and therapeutic challenge, being the third to fourth leading cause of cancer death, despite advances in diagnostic tools. This article presents a successful approach using a novel genomic analysis in the evaluation and treatment of a CUP patient, leveraging whole-exome sequencing (WES) and RNA sequencing (RNA-seq). The patient, with a history of multiple primary tumors including urothelial cancer, exhibited a history of rapid progression on empirical chemotherapy. The application of our approach identified a molecular target, characterized the tumor expression profile and the tumor microenvironment, and analyzed the origin of the tumor, leading to a tailored treatment. This resulted in a substantial radiological response across all metastatic sites and the predicted primary site of the tumor. We argue that a comprehensive genomic and molecular profiling approach, like the BostonGene© Tumor Portrait, can provide a more definitive, personalized treatment strategy, overcoming the limitations of current predictive assays. This approach offers a potential solution to an unmet clinical need for a standardized approach in identifying the tumor origin for the effective management of CUP

Hepatic angiosarcomatous transformation of a mediastinal germinal cell tumor: A care case report

Rationale: Mediastinal nonseminomatous germ cell tumor (NSGCT) is an uncommon entity. Metastatic hepatic sarcomatous transformation is rare. Patient concerns: We report a 24-year-old man with no previous related medical history presented with chest pain and left arm numbness. Diagnoses: The X-ray showed an anterior mediastinal mass. The chest computed tomography (CT) confirmed the presence of a mildly enhancing mass in the same location, without invasion of any vascular structure. A CT-guided biopsy was performed, revealing a primary mediastinal nonseminomatous germ cell tumor (NSGCT), yolk sac histology, with areas of somatic transformation to malignant nerve sheath tumor. After surgery patient was followed-up with imaging. Two years later a CT scan showed a new hepatic hyper vascular lesion, confirmed by a subsequent magnetic resonance imaging (MRI) and positron emission tomography (PET) scan. A CT-guided biopsy revealed a hepatic metastatic transformation to angiosarcoma of the primitive NSGCT. Interventions: The patient went on to received palliative chemotherapy. Outcomes: The patient is being followed-up regularly at the outpatient department. Lessons: Because of the potential of metastatic sarcoma arising from germ cell tumors, these patients should undergo periodical follow-up, with periodical scans. PET\CT scan might have a role in the follow-up of these patients

Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients - Fig 2

<p>(a) Extracted CT slice after acquisition, (b) magnified view of tumor region with (top) and without (bottom) the manually drawn boundary, (c) 3-D view of manually segmented pancreas with tumor, (d) 2-D slices of tumor.</p

Exemplar tumors with rendered texture features displayed by converting data into gray levels with range [0, 255].

<p>Resultant matrices rendered from GLCM, RLM, ACM1, and ACM2. Histogram used in the derivation of IH features. LBP and FD values at each pixel. Gradient angle computed with Sobel operator on each pixel used in ACM1 and ACM2 features. Gradient magnitude computed with Sobel operator on each pixel used in ACM2 features.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with the proposed method using leave-one-image-out technique.

<p>The maximum <i>AUC</i> and <i>Ac</i> were highlighted with bold face. ‘***’ corresponds no outcome due to no features selected.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with fMRMR feature selection and naive Bayes classification using leave-one-image-out and three-fold cross-validation techniques.

<p>The maximum AUC and <i>Ac</i> are highlighted with bold face.</p

ROC curves obtained with different feature sets extracted from the tumor region using (a) leave-one-image-out and (b) three-fold cross-validation techniques.

<p>ROC curves obtained with different feature sets extracted from the tumor region using (a) leave-one-image-out and (b) three-fold cross-validation techniques.</p

Correlation of pre-treatment patient factors with survival.

<p>Correlation of pre-treatment patient factors with survival.</p