Hypothesis Quiz Selected Answers Summer 2024
CS7641: Machine Learning
Prepared by: Theodore J. LaGrow
Last Updated: 05/29/24
The following are curated answers generated by the Teaching Staff for the article, “Robust T-Loss for
Medical Image Segmentation” by Gonzalez-Jimenez et al. I have tried to highlight key details needed for
each question. These answers are examples in no particular order.
Question 1
Much like Rule 6 in Ten simple rules for structuring papers by Konrad Kording and Brett Mensh, what is
the gap the authors provide? Please provide 400 words or less.
• For this question, we are really looking for what the author’s claim for the gap as well as some
explanation for why this is the gap. For full completeness, you need to give explanation or
evidence.
Responses:
The training sets currently used for medical image segmentation are noisy and incorrectly labeled in
about 5% of the data. The gap that the author’s set out to resolve is developing a new deep learning
algorithm for medical image segmentation that is less sensitive to the quality of the training set
compared to previous methods which include Convolutional Neural Networks (CNNs) and Visual
Transformers (ViTs). In the authors’ opinion, the solution to this gap is using a modified version of
robust loss functions described in the paper, named T-Loss.
---
In this paper, the authors address a critical gap in the field of medical image segmentation,
specifically related to handling outliers and noisy labels. It sets up the context about medical image
segmentation and the significance of regions of interest being affected by noise of varied levels and
types. It also talks about traditional loss functions and their ability to handle outliers in a robust
manner, or the lack thereof. The proposed solution is the T-Loss, a novel loss function based on the
negative log-likelihood of the Student-t distribution. It is promised to be a simpler and more efficient
loss function which is then backed up using experimental results with the Dice scores as the metric
for segmentation accuracy. By addressing this gap, the authors contribute to improving the reliability
and accuracy of medical image segmentation models.
---
, Rule 6 of the “Ten Simple Rules” document addresses the paper’s purpose. More specifically,
communicating to the reader how the new technology solves an existing problem and why the
solution is important. The T-Loss paper outlines a common problem of using Convolutional Neural
Networks (CNNs) and Visual Transformers (ViTs) of obtaining large amounts of training data. In
medical imaging the data is costly due to labeling each pixel which requires human expertise. One
possible solution is to obtain labels through automated mining or crowd-sourcing methods. This
method produces data labels with a high level of noise. To correct the noisy labels several approaches
have been studied such as label correction, estimated noise transition matrix, and robust loss
function (the purpose of the paper). The robust loss function seems the most promising but is
understudied. This paper helps fill the gap of analyzing several traditional robust loss functions and a
new one, T-Loss. The paper does a good job of structuring research on robust loss by covering
previous loss solutions, introducing a new loss (T-Loss), clearly outlining the datasets and metrics
used the experiments to show results and discussing findings. The paper does fill the “gap” of adding
research of robust loss in medical imaging and lays out a clear blueprint to continue adding the body
of research on the topic.
---
The gap that the authors provide is the need for large amounts of annotated data when developing
state-of-the-art segmentation models. The authors say that "supervised training of CNNs and ViTs
requires large amounts of annotated data" and specifically for the medical domain, that "obtaining
these annotations can be affected by human bias and poor inter-annotator agreement". Additionally,
the authors claim that the quality of the collected datasets is difficult because there is a large amount
of noise induced in the data. All of these aforementioned reasons stated by the authors contribute to
the gap that the authors are trying to close with this paper.
---
Field gap: Large amount of annotated training data for medical image segmentation is hard to obtain
and the annotations can be affected by multiple reasons causing the data with high levels of noise.
Subfield gap: Robust loss function provides a simpler solution with a single modeling component.
Other approaches have limitations such as more hyper-parameters, or complex training procedures.
Gap within the subfield: Traditional robust loss functions are vulnerable to memorizing noisy labels.
Field gap: Large amount of annotated training data for medical image segmentation is hard to obtain
and the annotations can be affected by multiple reasons causing the data with high levels of noise.
Subfield gap: Robust loss function provides a simpler solution with a single modeling component.
Other approaches have limitations such as more hyper-parameters, or complex training procedures.
Gap within the subfield: Traditional robust loss functions are vulnerable to memorizing noisy labels.
CS7641: Machine Learning
Prepared by: Theodore J. LaGrow
Last Updated: 05/29/24
The following are curated answers generated by the Teaching Staff for the article, “Robust T-Loss for
Medical Image Segmentation” by Gonzalez-Jimenez et al. I have tried to highlight key details needed for
each question. These answers are examples in no particular order.
Question 1
Much like Rule 6 in Ten simple rules for structuring papers by Konrad Kording and Brett Mensh, what is
the gap the authors provide? Please provide 400 words or less.
• For this question, we are really looking for what the author’s claim for the gap as well as some
explanation for why this is the gap. For full completeness, you need to give explanation or
evidence.
Responses:
The training sets currently used for medical image segmentation are noisy and incorrectly labeled in
about 5% of the data. The gap that the author’s set out to resolve is developing a new deep learning
algorithm for medical image segmentation that is less sensitive to the quality of the training set
compared to previous methods which include Convolutional Neural Networks (CNNs) and Visual
Transformers (ViTs). In the authors’ opinion, the solution to this gap is using a modified version of
robust loss functions described in the paper, named T-Loss.
---
In this paper, the authors address a critical gap in the field of medical image segmentation,
specifically related to handling outliers and noisy labels. It sets up the context about medical image
segmentation and the significance of regions of interest being affected by noise of varied levels and
types. It also talks about traditional loss functions and their ability to handle outliers in a robust
manner, or the lack thereof. The proposed solution is the T-Loss, a novel loss function based on the
negative log-likelihood of the Student-t distribution. It is promised to be a simpler and more efficient
loss function which is then backed up using experimental results with the Dice scores as the metric
for segmentation accuracy. By addressing this gap, the authors contribute to improving the reliability
and accuracy of medical image segmentation models.
---
, Rule 6 of the “Ten Simple Rules” document addresses the paper’s purpose. More specifically,
communicating to the reader how the new technology solves an existing problem and why the
solution is important. The T-Loss paper outlines a common problem of using Convolutional Neural
Networks (CNNs) and Visual Transformers (ViTs) of obtaining large amounts of training data. In
medical imaging the data is costly due to labeling each pixel which requires human expertise. One
possible solution is to obtain labels through automated mining or crowd-sourcing methods. This
method produces data labels with a high level of noise. To correct the noisy labels several approaches
have been studied such as label correction, estimated noise transition matrix, and robust loss
function (the purpose of the paper). The robust loss function seems the most promising but is
understudied. This paper helps fill the gap of analyzing several traditional robust loss functions and a
new one, T-Loss. The paper does a good job of structuring research on robust loss by covering
previous loss solutions, introducing a new loss (T-Loss), clearly outlining the datasets and metrics
used the experiments to show results and discussing findings. The paper does fill the “gap” of adding
research of robust loss in medical imaging and lays out a clear blueprint to continue adding the body
of research on the topic.
---
The gap that the authors provide is the need for large amounts of annotated data when developing
state-of-the-art segmentation models. The authors say that "supervised training of CNNs and ViTs
requires large amounts of annotated data" and specifically for the medical domain, that "obtaining
these annotations can be affected by human bias and poor inter-annotator agreement". Additionally,
the authors claim that the quality of the collected datasets is difficult because there is a large amount
of noise induced in the data. All of these aforementioned reasons stated by the authors contribute to
the gap that the authors are trying to close with this paper.
---
Field gap: Large amount of annotated training data for medical image segmentation is hard to obtain
and the annotations can be affected by multiple reasons causing the data with high levels of noise.
Subfield gap: Robust loss function provides a simpler solution with a single modeling component.
Other approaches have limitations such as more hyper-parameters, or complex training procedures.
Gap within the subfield: Traditional robust loss functions are vulnerable to memorizing noisy labels.
Field gap: Large amount of annotated training data for medical image segmentation is hard to obtain
and the annotations can be affected by multiple reasons causing the data with high levels of noise.
Subfield gap: Robust loss function provides a simpler solution with a single modeling component.
Other approaches have limitations such as more hyper-parameters, or complex training procedures.
Gap within the subfield: Traditional robust loss functions are vulnerable to memorizing noisy labels.
